An executive’s guide to AI | Leaders in Pharmaceutical Business Intelligence Group, LLC, Doing Business As LPBI Group, Newton, MA

https://pharmaceuticalintelligence.com/2025/11/15/authentic-relevance-of-lpbi-groups-portfolio-of-ip-as-proprietary-training-data-corpus-for-ai-initiatives-at-big-pharma/

AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology

Posted in An executive's guide to AI, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Deep Learning, IP Development by LPBI Group Team, LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, Machine Learning, Natural Language Processing (NLP) on November 22, 2025| Leave a Comment »

AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology

Curator: Aviva Lev-Ari, PhD, RN

We had researched the topic of AI Initiatives in Big Pharma in the following article:

Authentic Relevance of LPBI Group’s Portfolio of IP as Proprietary Training Data Corpus for AI Initiatives at Big Pharma

We are publishing a Series of Five articles that demonstrate the Authentic Relevance of Five of the Ten Digital IP Asset Classes in LPBI Group’s Portfolio of IP for AI Initiatives at Big Pharma.

For the Ten IP Asset Classes in LPBI Group’s Portfolio, See

https://pharmaceuticalintelligence.com/portfolio-of-ip-assets/

The following Five Digital IP Asset classes are positioned as Proprietary Training Data and Inference for Foundation Models in Health care.

https://pharmaceuticalintelligence.com/healthcare-foundation-model/

This Corpus comprises of Live Repository of Domain Knowledge Expert-Written Clinical Interpretations of Scientific Findings codified in the following five Digital IP ASSETS CLASSES:

• IP Asset Class I: Journal: PharmaceuticalIntelligence.com

6,250 scientific articles (70% curations, creative expert opinions. 30% scientific reports).

2.4MM Views, equivalent of $50MM if downloading an article is paid market rate of $30.

PharmaceuticalIntelligence.com Journal – Projecting the Annual Rate of Article Views

https://pharmaceuticalintelligence.com/vision/pharmaceuticalintelligence-com-journal-projecting-the-annual-rate-of-article-views/

• IP Asset Class II: 48 e-Books: English Edition & Spanish Edition.

152,000 pages downloaded under pay-per-view. The largest number of downloads for one e-Publisher (LPBI)

https://www.amazon.com/s?k=Aviva+Lev-Ari&i=digital-text&rh=n%3A133140011&ref=nb_sb_noss

• IP Asset Class III: 100 e-Proceedings and 50 Tweet Collections of Top Biotech and Medical Global Conferences, 2013-2025

https://pharmaceuticalintelligence.com/press-coverage/part-three-conference-eproceedings-deliverables-social-media-analytics/

• IP Asset Class V: 7,500 Biological Images in our Digital Art Media Gallery, as prior art. The Media Gallery resides in WordPress.com Cloud of LPBI Group’s Web site

PharmaceuticalIntelligence.com

• IP Asset Class X: +300 Audio Podcasts: Interviews with Scientific Leaders

https://pharmaceuticalintelligence.com/biomed-audio-podcast-library-lpbi-group/

BECAUSE THE ABOVE ASSETS ARE DIGITAL ASSETS they are ready for use as Proprietary TRAINING DATA and INFERENCE for AI Foundation Models in HealthCare.

Expert‑curated healthcare corpus mapped to a living ontology, already packaged for immediate model ingestion and suitable for safe pre-training, evals, fine‑tuning and inference. If healthcare domain data is on your roadmap, this is a rare, defensible asset.

The article TITLE of each of the five Digital IP Asset Classes matched to AI Initiatives in Big Pharma, an article per IP Asset Class are:

AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology

https://pharmaceuticalintelligence.com/2025/11/22/ai-initiatives-in-big-pharma-grog-prompt-proprietary-data-training-and-inference-by-lpbi-groups-ip-asset-class-i-pharmaceuticalintelligence-com-journal-2-5mm-views-6250-scientific-article/

Article conclusions by @Grok

Conclusions and Implications

The article concludes that LPBI’s assets are primed for Big Pharma adoption, enabling AI to evolve from tools to “knowledge amplifiers.” It hints at licensing opportunities without explicit calls to action. Technical Implications: Improves AI precision in complex pharma tasks like rare disease modeling. Business Implications: Accelerates ROI on AI investments (e.g., Pfizer’s $500M push) while ensuring defensible IP. Unique Insight: In an era of open-source AI, curated expert data like this is a “rare moat” for competitive edge.Overall, the piece is insightful for AI-pharma intersections, blending real-world examples with promotional asset positioning. It’s dense with links to LPBI resources (e.g., e-books, podcasts) for deeper dives.

AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view

https://pharmaceuticalintelligence.com/2025/11/22/ai-initiatives-in-big-pharma-grog-prompt-proprietary-training-data-and-inference-by-lpbi-groups-ip-asset-class-ii-48-e-books-english-edition-spanish-edition-152000/

AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class III: 100 e-Proceedings and 50 Tweet Collections of Top Biotech and Medical Global Conferences, 2013-2025

https://pharmaceuticalintelligence.com/2025/11/22/ai-initiatives-in-big-pharma-grog-prompt-proprietary-training-data-and-inference-by-lpbi-groups-ip-asset-class-iii-100-e-proceedings-and-50-tweet-collections-of-top-biotech/

AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class V: 7,500 Biological Images in LPBI Group’s Digital Art Media Gallery, as Prior Art

https://pharmaceuticalintelligence.com/2025/11/22/ai-initiatives-in-big-pharma-grog-prompt-proprietary-training-data-and-inference-by-lpbi-groups-ip-asset-class-v-7500-biological-images-in-lpbi-groups-digital-art/

AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class X: +300 Audio Podcasts Library: Interviews with Scientific Leaders

https://pharmaceuticalintelligence.com/2025/11/22/ai-initiatives-in-big-pharma-grog-prompt-proprietary-training-data-and-inference-by-lpbi-groups-ip-asset-class-x-300-audio-podcasts-library-interviews-with-scientific-leaders/

In the series of five articles, as above, we are presenting the key AI Initiatives in Big Pharma as it was created by our prompt to @Grok on 11/18/2025:

What are PFIZER’s AI INITIATIVES?

@Grok Response:

x.com/i/grok/share/0ol5VOJsEYs11baXq4xkzNb0h

Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis.

Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines

Clinical Trials and Regulatory Efficiency AI:

-Predictive Regulatory Tools

-Decentralize Trials

-inventory management

Disease Detection and Diagnostics:

– ATTR-CM Initiative

– Rare diseases

Generative AI and Operational Tools:

– Charlie Platform

– Scientific Data Cloud AWS powered ML on centralized data

– Amazon’s SageMaker /Bedrock for Manufacturing efficiency

– Global Health Grants:

Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care

Partnerships and Education

– Collaborations: IMI Big Picture for 3M – sample disease database

– AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine

– Webinars of AI for biomedical data integration

– Webinar on AI in Manufacturing

Strategic Focus:

– $500M R&D reinvestment by 2026 targets AI for Productivity

– Part of $7.7B cost savings

– Ethical AI, diverse DBs

– Global biotech advances: China’s AI in CRISPR

AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology

The Left Column was written @Grok

The Right Column was written by Aviva Lev-Ari, PhD, RN

AI Initiative at Big Pharma

i.e., Pfizer

Journal articles

N = 6,250

Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis.

Generative AI tools searching LPBI’s Proprietary data in addition to Public Domain data sources

Journal ONTOLOGY used to optimize context classification selected for search

Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines

– Run prompts by category of research on three dyads

– Run ML across categories of research for these three dyads

-Gene-disease

-Disease-drug

-Gene-drug

Clinical Trials and Regulatory Efficiency AI:

-Predictive Regulatory Tools

-Decentralize Trials

-inventory management

content creation across the drug lifecycle, from lab data to regulatory docs

Disease Detection and Diagnostics:

– ATTR-CM Initiative

– Rare diseases

Heart Failure Diagnosis by Early detection of transthyretin amyloid cardiomyopathy

– Journal published ~30 curations by Dr. Larry on this subject

– Run NLP on this Corpus

Rare disease:

Journal published 560 articles on Rare diseases

– Run ML on this Corpus

Generative AI and Operational Tools:

– Charlie Platform

– Scientific Data Cloud AWS powered ML on centralized data

– Amazon’s SageMaker /Bedrock for Manufacturing efficiency

– Global Health Grants:

Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care

Content generated in the Journal can become “generated compliant content” if run by the

Charlie Platform.

– For REUSE content in context

Entire Corpus of 9 Giga bytes can be ingested to Pfizer Foundation’s AI Learning Lab

– Rum prompts against it

– Journal’s Content to be used for Internal staff expertise development

– Journal’s Content for Leadership development

Partnerships and Education

– Collaborations: IMI Big Picture for 3M – sample disease database

– AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine

– Webinars of AI for biomedical data integration

– Webinar on AI in Manufacturing

The Journal had published 547 articles in Precision Medicine

The Journal had published 1,114 articles in Drug Discovery

The Journal had published 701 articles in Drug Delivery

The Journal had published 3,615 articles on subject matter “Disease”

The Journal had published 738 articles on Biomedical topic

The Journal had published 425 articles in Artificial Intelligence

Strategic Focus:

– $500M R&D reinvestment by 2026 targets AI for Productivity

– Part of $7.7B cost savings

– Ethical AI, diverse DBs

– Global biotech advances: China’s AI in CRISPR

The Journal had published 432 articles on CRISPR

Productivity derived from Journal’s content:

– As a result of the fact that ~70% of the Journal’s articles are curations written by Domain Knowledge Experts subjectively expressing theirs clinical interpretations of basic and primary research – the productivity of the knowledge workers at any Big Pharma would increase vastly.

– If Grok and Claude would run on LPBI Group’s Corpus – A scientific revolution will emerge

– It is not combinatorics applied to molecules with 98% futile results!!! – it is the IQ of Gifted HUMANS, of domain knowledge experts generating content using individual CREATIVITY no Quantum or Super Intelligence which is not in existence YET.

– Foundation Models in Healthcare depends on the OUTPUT of the human creative mind. AI takes keyword (classic search) and concepts (semantic search) and run frequency of occurrence, predict the nest word, one after the next one.

CONCLUSIONS: The Voice of Dr. Stephen J. Williams PhD

PENDING

@grok Summary of this article:

AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology https://pharmaceuticalintelligence.com/2025/11/22/ai-initiatives-in-big-pharma-grog-prompt-proprietary-data-training-and-inference-by-lpbi-groups-ip-asset-class-i-pharmaceuticalintelligence-com-journal-2-5mm-views-6250-scientific-article/

Summary of the ArticleTitle:

Publication Date: November 22, 2025

Author/Curator: Aviva Lev-Ari, PhD, RN

@Grok SOURCE:

https://x.com/i/grok?conversation=1994759142053908719

Overview: This article, part of a series on LPBI Group’s digital IP assets, explores how the group’s proprietary content—particularly IP Asset Class I (PharmaceuticalIntelligence.com Journal)—can serve as high-value training data for AI models in the pharmaceutical industry. It uses a Grok AI prompt to detail Pfizer’s AI efforts and maps them to the journal’s expert-curated content, emphasizing the shift from generic AI to domain-specific, human-enhanced models for drug discovery, clinical trials, and beyond. The piece positions LPBI’s assets as “defensible” resources for pre-training, fine-tuning, and inference in healthcare foundation models, contrasting them with public-domain data.Main Thesis and Key Arguments

Core Idea: Traditional AI in pharma often relies on combinatorial predictions from vast but uncurated data, leading to inefficiencies. LPBI’s corpus, with 70% expert interpretations (curations and opinions), injects human creativity and clinical insights, enabling more productive AI outputs. This could spark a “scientific revolution” by powering models like Grok or Claude, boosting knowledge worker efficiency and reducing R&D costs.
Value Proposition: The journal’s 6,250 articles (30% scientific reports, 70% expert analyses) form a 9GB digital repository with a live ontology for semantic mapping. Valued at ~$50MM (based on $30/article download equivalent), it supports AI applications across gene-disease-drug dyads, NLP for rare diseases, and regulatory content generation.
Broader Context: Part of a five-article series showcasing LPBI’s ten IP classes (e.g., e-books, podcasts, images) as ready-to-ingest assets for Big Pharma’s AI strategies.

AI Initiatives in Big Pharma (Focus on Pfizer)The article leverages a November 18, 2025, Grok prompt (“What are PFIZER’s AI INITIATIVES?”) to outline Pfizer’s efforts, sourced from real-time AI generation. Key highlights include:

Initiative Category	Description	Key Metrics/Examples
Generative AI Tools	Automates literature searches, data analysis, and report writing.	Saves 16,000 hours annually; integrates with Pfizer’s Charlie Platform for compliant content.
Drug Discovery Acceleration	Uses ML, supercomputing, and AI to identify targets and optimize molecules.	Part of $500M R&D reinvestment by 2026; contributes to $7.7B in cost savings.
Clinical Trials & Regulatory Efficiency	Predictive modeling for patient recruitment, decentralized trials, and inventory management.	Partnerships like IMI Big Picture; AI for faster FDA submissions.
Disease Detection & Diagnostics	Focus on rare diseases (e.g., ATTR-CM via transthyretin amyloid cardiomyopathy initiative).	NLP and ML for early detection; equitable care via AI Learning Lab.
Operational & Strategic Tools	Scientific Data Cloud with AWS ML/SageMaker; ethical AI with diverse datasets.	Webinars for education; global expansions (e.g., China’s CRISPR AI).

No other pharma companies are deeply covered, but the framework implies applicability to peers like those in prior LPBI articles.Mapping to LPBI’s Proprietary DataA central table links Pfizer’s initiatives to the journal’s content, demonstrating ingestion potential:

Pfizer AI Initiative	LPBI Journal Alignment	Productivity Boost
Generative AI for searches	Ontology-optimized semantic searches across 6,250 articles.	Enhances accuracy in 425 AI-related articles.
Drug Discovery ML	1,114 articles on discovery; dyad mapping (e.g., gene-drug).	Supports ML training on precision medicine (547 articles).
Clinical Trials Efficiency	3,615 “Disease” articles; NLP for trial simulations.	Reduces time via expert-curated protocols.
Rare Disease Diagnostics	Content on ATTR-CM and similar (e.g., 432 CRISPR articles).	Fine-tuning for targeted inference.
Operational Platforms	Full corpus ingestion for Charlie-like labs.	2.5MM views indicate high-engagement data.

This shows how the live ontology enables context classification, outperforming frequency-based AI.Methodologies and Frameworks

AI Training Pipeline: Ingest corpus for pre-training (broad knowledge), fine-tuning (domain-specific), and inference (real-time queries). Use prompts by research categories; apply ML across dyads; generate lifecycle content.
Productivity Model: 70% expert curations amplify human-AI synergy, avoiding “futile” combinatorial results. Ethical focus: Diverse, compliant data for global equity.
Insights: Quotes Dr. Stephen J. Williams: Foundation models thrive on “gifted humans’ creativity,” not just scale. Predicts vast productivity gains (e.g., beyond Pfizer’s 16,000 hours saved).

Conclusions and Implications

The article concludes that LPBI’s assets are primed for Big Pharma adoption, enabling AI to evolve from tools to “knowledge amplifiers.” It hints at licensing opportunities without explicit calls to action. Technical Implications: Improves AI precision in complex pharma tasks like rare disease modeling. Business Implications: Accelerates ROI on AI investments (e.g., Pfizer’s $500M push) while ensuring defensible IP. Unique Insight: In an era of open-source AI, curated expert data like this is a “rare moat” for competitive edge.Overall, the piece is insightful for AI-pharma intersections, blending real-world examples with promotional asset positioning. It’s dense with links to LPBI resources (e.g., e-books, podcasts) for deeper dives.

If you’d like a full review (e.g., strengths/weaknesses, comparisons to industry trends), or analysis of specific sections, let me know!

https://event.technologyreview.com/emtech-digital-2022/speakers

2022 EmTechDigital @MIT, March 29-30, 2022

Posted in An executive's guide to AI, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence in Medicine - Applications in Therapeutics, Conference Coverage with Social Media, Deep Learning in Pathology, Machine Learning, Natural Language Processing (NLP), Scientific & Biotech Conferences: Press Coverage on March 28, 2022| Leave a Comment »

2022 EmTechDigital @MIT, March 29-30, 2022

Real Time Coverage: Aviva Lev-Ari, PhD, RN

#EmTechDigital

@AVIVA1950

@pharma_BI

@techreview

SPEAKERS

Ali
Alvi

Turing Group Program Manager

Microsoft

Refik
Anadol

CEO, RAS Lab; Lecturer

UCLA

Lauren
Bennett

Group Software Engineering Lead, Spatial Analysis and Data Science

Esri

Elizabeth
Bramson-Boudreau

CEO

MIT Technology Review

Tara
Chklovski

Founder & CEO

Technovation

Sheldon
Fernandez

CEO

DarwinAI

David
Ferrucci

Founder, CEO, & Chief Scientist

Elemental Cognition

Anthony
Green

Podcast Producer

MIT Technology Review

Agrim
Gupta

PhD Student, Stanford Vision and Learning Lab

Stanford University

Mike
Haley

VP of Research

Autodesk

Will Douglas
Heaven

Senior Editor for AI

MIT Technology Review

Natasha
Jaques

Senior Research Scientist

Google Brain

Tony
Jebara

VP of Engineering and Head of Machine Learning

Spotify

Clinton
Johnson

Racial Equity Unified Team Lead

Esri

Danny
Lange

SVP of Artificial Intelligence

Unity Technologies

Julia (Xing)
Li

Deputy General Manager

Baidu USA

Darcy
MacClaren

Senior Vice President, Digital Supply Chain

SAP North America

Haniyeh
Mahmoudian

Global AI Ethicist

DataRobot

Andrew
Moore

GM and VP, Google Cloud AI

Google

Mira
Murati

SVP, Research, Product, & Partnerships

OpenAI

Prem
Natarajan

Vice President Alexa AI, Head of NLU

Amazon

Andrew
Ng

Founder and CEO

Landing AI

Amy
Nordrum

Editorial Director, Special Projects & Operations

MIT Technology Review

Kavitha
Prasad

VP & GM, Datacenter, AI and Cloud Execution and Strategy

Intel Corporation

Bali
Raghavan

Head of Engineering

Forward

Rajiv
Shah

Principal Data Scientist

Snorkel AI

Sameena
Shah

Managing Director, J.P. Morgan AI Research

JP Morgan Chase

David
Simchi-Levi

Director, Data Science Lab

MIT

Jennifer
Strong

Senior Editor for Podcasts and Live Journalism

MIT Technology Review

Fiona
Tan

CTO

Wayfair

Zenna
Tavares

Research Scientist, Columbia University; Co-Founder

Basis

Nicol
Turner Lee

Director, Center for Technology Innovation

Brookings Institution

Raquel
Urtasun

Founder & CEO

Waabi

Oriol
Vinyals

Principal Scientist

DeepMind

MIT Inside Track

David
Cox

IBM Director

MIT-IBM Watson AI Lab

Luba
Elliott

Curator, Producer, and Researcher

Creative AI

Charlotte
Jee

Reporter, News

MIT Technology Review

Naveen
Kamat

Executive Director, Data and AI Services

Kyndryl

Joseph
Lehar

Senior Vice President, R&D Strategy

Owkin

Stefanie
Mueller

Associate Professor

MIT CSAIL

Jianxiong
Xiao

Founder and CEO

AutoX

TUESDAY, MARCH 29

Data-Centric AI

Better Data, Better AI

Data powers AI. Good data can mean the difference between an impactful solution or one that never gets off the ground. Re-assess the foundational AI questions to ensure your data is working for, not against, you.

Innovation to Reality

The challenges of implementing AI are many. Avoid the common pitfalls with real-world case studies from leaders who have successfully turned their AI solutions into reality.

Harness What’s Possible at the Edge

With its potential for near instantaneous decision making, pioneers are moving AI to the edge. We examine the pros and cons of moving AI decisions to the edge, with the experts getting it right.

Generative AI Solutions

The use of generative AI to boost human creativity is breaking boundaries in creative areas previously untouched by AI. We explore the intersection of data and algorithms enabling collaborative AI processes to design and create.

Day 1: Data-Centric AI (9:00 a.m. – 5:20 p.m.)

9:00 AM

Welcome Remarks

Will Douglas Heaven

Senior Editor for AI, MIT Technology Review

Better Data, Better AI (9:10 a.m. – 10:35 a.m.)

9:10 AM

Empowering Data-Centric AI

Data is the most under-valued and de-glamorized aspect of AI. Learn why shifting the focus from model/algorithm development to quality of the data is the next and most efficient, way to improve the decision-making abilities of AI.

Andrew Ng

Founder and CEO, Landing AI

9:40 AM

The Mechanics of Data-First AI

Data labeling is key to determining the success or failure of AI applications. Learn how to implement a data-first approach that can transform AI inference, resulting in better models that make better decisions.

Rajiv Shah

Principal Data Scientist, Snorkel AI

10:10 AM

Thought Leadership in Responsible AI

Question the status quo. Build stakeholder trust. These are foundational elements of thought leadership in AI. Explore how organizations can use their data and algorithms in ethical and responsible ways while building bigger and more effective systems.

Haniyeh Mahmoudian

Global AI Ethicist, DataRobot

Mainstage Break (10:35 a.m. – 11:05 a.m.)

Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.

10:35 AM

MIT Inside Track: From AI Startup to Tech “Unicorn” (available online only)

With its next-generation machine learning models fueling precision medicine, French biotech company, Owkin, captured the attention of the pharma industry. Learn how they did it and get tips to navigate the complex task of scaling your innovation.

Joseph Lehar

Senior Vice President, R&D Strategy, Owkin

Secrets of Successful AI Deployments

Networking and refreshments for our live audience.

Innovation to Reality (11:05 a.m. – 12:30 p.m.)

The challenges of implementing AI are many. Avoid the common pitfalls with real-world case studies from leaders who have successfully turned their AI solutions into reality.

11:05 AM

Deploying AI in real-world environments benefits from human input before and during implementation. Get an inside look at how organizations can ensure reliable results with the key questions and competing needs that should be considered when implementing AI solutions.

Andrew Moore

GM and VP, Google Cloud AI, Google

11:35 AM

From Research Lab to Real World

AI is evolving from the research lab into practical real world applications. Learn what issues should be top of mind for businesses, consumers, and researchers as we take a deep dive into AI solutions that increase modern productivity and accelerate intelligence transformation.

Julia (Xing) Li

Deputy General Manager, Baidu USA

12:00 PM

Closing the 20% Performance Gap

Getting AI to work 80% of the time is relatively straightforward, but trustworthy AI requires deployments that work 100% of the time. Unpack some of the biggest challenges that come up when eliminating the 20% gap.

Bali Raghavan

Head of Engineering, Forward

Lunch and Networking Break (12:30 p.m. – 1:30 p.m.)

12:30 PM

Lunch and Networking Break

Lunch served at the MIT Media Lab and a selection of curated content for those tuning in virtually.

Harness What’s Possible at the Edge (1:30 p.m. – 3:15 p.m.)

With its potential for near instantaneous decision making, pioneers are moving AI to the edge. We examine the pros and cons of moving AI decisions to the edge, with the experts getting it right.

1:30 PM

AI Integration Across Industries – Presented by Intel

To create sustainable business impact, AI capabilities need to be tailored and optimized to an industry or organization’s specific requirements and infrastructure model. Hear how customers’ challenges across industries can be addressed in any compute environment from the cloud to the edge with end-to-end hardware and software optimization.

Kavitha Prasad

VP & GM, Datacenter, AI and Cloud Execution and Strategy, Intel Corporation

Elizabeth Bramson-Boudreau

CEO, MIT Technology Review

1:55 PM

Explainability at the Edge

Decision making has moved from the edge to the cloud before settling into a hybrid setup for many AI systems. Through the examination of key use-cases, take a deep dive into understanding the benefits and detractors of operating a machine-learning system at the point of inference.

Sheldon Fernandez

CEO, DarwinAI

2:25 PM

AI Experiences at the Edge

Enable your organization to transform customer experiences through AI at the edge. Learn about the required technologies, including teachable and self-learning AI, that are needed for a successful shift to the edge, and hear how deploying these technologies at scale can unlock richer, more responsive experiences.

Prem Natarajan

Vice President Alexa AI, Head of NLU, Amazon

2:50 PM

The Road Ahead

Reimagine AI solutions as a unified system, instead of individual components. Through the lens of autonomous vehicles, discover the pros and cons of using an all-inclusive AI-first approach that includes AI decision-making at the edge and see how this thinking can be applied across industry.

Raquel Urtasun

Founder & CEO, Waabi

Mainstage Break (3:15 p.m. – 3:45 p.m.)

Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.

3:15 PM

MIT Inside Track: The Impact of Creative AI (available online only)

Networking and refreshments for our live audience.

Advances in machine learning are enabling artists and creative technologists to think about and use AI in new ways. Discuss the concept of creative AI and look at project examples from London’s art scene that illustrate the various ways creative AI is bridging the gap between the traditional art world and the latest technological innovations.

Luba Elliott

Curator, Producer, and Researcher, Creative AI

Generative AI Solutions (3:45 p.m. – 5:10 p.m.)

3:45 PM

Enhancing Design through Generative AI

Change the design problem with AI. The creative nature of generative AI enhances design capabilities, finding efficiencies and opportunities that humans alone might not conceive. Explore business applications including project planning, construction, and physical design.

Mike Haley

VP of Research, Autodesk

4:15 PM

Using Synthetic Data and Simulations

Deep learning is data hungry technology. Manually labelled training data has become cost prohibitive and time-consuming. Get a glimpse at how interactive large-scale synthetic data generation can accelerate the AI revolution, unlocking the potential of data-driven artificial intelligence.

Danny Lange

SVP of Artificial Intelligence, Unity Technologies

4:40 PM

The Art of AI

Push beyond the typical uses of AI. Explore the nexus of art, technology, and human creativity through the unique innovation of kinetic data sculptures that use machines to give physical context and shape to data to rethink how we engage with the physical world.

Refik Anadol

CEO, RAS Lab; Lecturer, UCLA

Last Call with the Editors (5:10 p.m. – 5:20 p.m.)

5:10 PM

Last Call with the Editors

Before we wrap day 1, join our last call with all of our editors to get their analysis on the day’s topics, themes, and guests.

Networking Reception (5:20 p.m. – 6:20 p.m.)

WEDNESDAY, MARCH 30

Evolving the Algorithms

What’s Next for Deep Learning

Deep learning algorithms have powered most major AI advances of the last decade. We bring you into the top innovation labs to see how they are advancing their deep learning models to find out just how much more we can get out of these algorithms.

AI in Day-To-Day Business

Many organizations are already using AI internally in their day-to-day operations, in areas like cybersecurity, customer service, finance, and manufacturing. We examine the tools that organizations are using when putting AI to work.

Making AI Work for All

As AI increasingly underpins our lives, businesses, and society, we must ensure that AI must work for everyone – not just those represented in datasets, and not just 80% of the time. Examine the challenges and solutions needed to ensure AI works fairly, for all.

Envisioning the Next AI

Some business problems can’t be solved with current deep learning methods. We look at what’s around the corner at the new approaches and most revolutionary ideas propelling us toward the next stage in AI evolution.

Day 2: Evolving the Algorithms (9:00 a.m. – 5:25 p.m.)

9:00 AM

Welcome Remarks

Will Douglas Heaven

Senior Editor for AI, MIT Technology Review

What’s Next for Deep Learning (9:10 a.m. – 10:25 a.m.)

9:10 AM

Transforming Traditional Algorithms

Transformer-based language models are revolutionizing the way neural networks process natural language. This deep dive looks at how organizations can put their data to work using transformer models. We consider the problems that business may face as these massive models mature, including training needs, managing parallel processing at scale, and countering offensive data.

Ali Alvi

Turing Group Program Manager, Microsoft

9:35 AM

Human-like Problem Solving

Critical thinking may be one step closer for AI by combining large-scale transformers with smart sampling and filtering. Get an early look at how AlphaCode’s entry into competitive programming may lead to a human-like capacity for AI to write original code that solves unforeseen problems.

Oriol Vinyals

Principal Scientist, DeepMind

10:00 AM

Aligning AI Technologies at Scale

As advanced AI systems gain greater capabilities in our search for artificial general intelligence, it’s critical to teach them how to understand human intentions. Look at the latest advancements in AI systems and how to ensure they can be truthful, helpful, and safe.

Mira Murati

SVP, Research, Product, & Partnerships, OpenAI

Mainstage Break (10:25 a.m. – 10:55 a.m.)

Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.

10:25 AM

Business-Ready Data Holds the Key to AI Democratization – Presented by Kyndryl

Networking and refreshments for our live audience.

Good data is the bedrock of a self-service data consumption model, which in turn unlocks insights, analytics, personalization at scale through AI. Yet many organizations face immense challenges setting up a robust data foundation. Dive into a pragmatic perspective on abstracting the complexity and untangling the conflicts in data management for better AI.

Naveen Kamat

Executive Director, Data and AI Services, Kyndryl

AI in Day-To-Day Business (10:55 a.m. – 12:20 p.m.)

10:55 AM

Improving Business Processes with AI

Effectively operationalized AI/ML can unlock untapped potential in your organization. From enhancing internal processes to managing the customer experience, get the pragmatic advice and takeaways leaders need to better understand their internal data to achieve impactful results.

Fiona Tan

CTO, Wayfair

11:25 AM

Accelerating the Supply Chain

Use AI to maximize reliability of supply chains. Learn the dos and don’ts to managing key processes within your supply chain, including workforce management, streamlining and simplification, and reaping the full value of your supply chain solutions.

Darcy MacClaren

Senior Vice President, Digital Supply Chain, SAP North America

David Simchi-Levi

Director, Data Science Lab, MIT

11:55 AM

Putting Recommendation Algorithms to Work

Machine and reinforcement learning enable Spotify to deliver the right content to the right listener at the right time, allowing for personalized listening experiences that facilitate discovery at a global scale. Through user interactions, algorithms suggest new content and creators that keep customers both happy and engaged with the platform. Dive into the details of making better user recommendations.

Tony Jebara

VP of Engineering and Head of Machine Learning, Spotify

Lunch and Networking Break (12:20 p.m. – 1:15 p.m.)

12:20 PM

Lunch and Networking Break

Lunch served at the MIT Media Lab and a selection of curated content for those tuning in virtually.

Making AI Work for All (1:15 p.m. – 2:35 p.m.)

1:15 PM

Mapping Equity

Walk through the practical steps to map and understand the nuances, outliers, and special cases in datasets. Get tips to ensure ethical and trustworthy approaches to training AI systems that grow in scope and scale within a business.

Lauren Bennett

Group Software Engineering Lead, Spatial Analysis and Data Science, Esri

Clinton Johnson

Racial Equity Unified Team Lead, Esri

1:45 PM

Bridging the AI Accessibility Gap

Get an inside look at the long- and short-term benefits of addressing inequities in AI opportunities, ranging from educating the tech youth of the future to a 10,000-foot view on what it will take to ensure that equity top is of mind within society and business alike.

Tara Chklovski

Founder & CEO, Technovation

2:10 PM

The AI Policies We Need

Public policies can help to make AI more equitable and ethical for all. Examine how policies could impact corporations and what it means for building internal policies, regardless of what government adopts. Identify actionable ideas to best move policies forward for the widest benefit to all.

Nicol Turner Lee

Director, Center for Technology Innovation, Brookings Institution

Mainstage Break (2:35 p.m. – 3:05 p.m.)

Networking and refreshments for our live audience and a selection of curated content for those tuning in virtually.

2:35 PM

MIT Inside Track: Accelerating the Advent of Autonomous Driving (available online only)

Networking and refreshments for our live audience.

From the U.S. to China, the global robo-taxi race is gaining traction with consumers and regulators alike. Go behind the scenes with AutoX – a Level 4 driving technology company – and hear how it overcame obstacles while launching the world’s second and China’s first public, fully driverless robo-taxi service.

Jianxiong Xiao

Founder and CEO, AutoX

Envisioning the Next AI (3:05 p.m. – 4:50 p.m.)

3:05 PM

How AI Is Powering the Future of Financial Services – Presented by JP Morgan Chase

The use of AI in finance is gaining traction as organizations realize the advantages of using algorithms to streamline and improve the accuracy of financial tasks. Step through use cases that examine how AI can be used to minimize financial risk, maximize financial returns, optimize venture capital funding by connecting entrepreneurs to the right investors; and more.

Sameena Shah

Managing Director, J.P. Morgan AI Research, JP Morgan Chase

3:30 PM

Evolution of Mind and Body

In a study of simulated robotic evolution, it was observed that more complex environments and evolutionary changes to the robot’s physical form accelerated the growth of robot intelligence. Examine this cutting-edge research and decipher what this early discovery means for the next generation of AI and robotics.

Agrim Gupta

PhD Student, Stanford Vision and Learning Lab, Stanford University

4:00 PM

A Path to Human-like Common Sense

Understanding human thinking and reasoning processes could lead to more general, flexible and human-like artificial intelligence. Take a close look at the research building AI inspired by human common-sense that could create a new generation of tools for complex decision-making.

Zenna Tavares

Research Scientist, Columbia University; Co-Founder, Basis

4:25 PM

Social Learning Bots

Look under the hood at this innovative approach to AI learning with multi-agent and human-AI interactions. Discover how bots work together and learn together through personal interactions. Recognize the future implications for AI, plus the benefits and obstacles that may come from this new process.

Natasha Jaques

Senior Research Scientist, Google Brain

Closing Segment (4:50 p.m. – 5:25 p.m.)

4:50 PM

Pulling Back the Curtain on AI

David Ferrucci was the principal investigator for the team that led IBM Watson to its landmark Jeopardy success, awakening the world to the possibilities of AI. We pull back the curtain on AI for a wide-ranging discussion on explicable models, and the next generation of human and machine collaboration creating AI thought partners with limitless applications.

David Ferrucci

Founder, CEO, & Chief Scientist, Elemental Cognition

5:15 PM

Closing Remarks

Closing Toast (5:25 p.m. – 5:45 p.m.)

http://businessoverbroadway.com/2021/02/01/machine-learning-adoption-rates-around-the-world/

Countries that are early adopters of ML methods

Posted in An executive's guide to AI, Artificial Intelligence - General, Machine Learning on February 2, 2021| Leave a Comment »

Countries that are early adopters of ML methods

Reporter: Aviva Lev-Ari, PhD, RN

Machine Learning Adoption by Country

Results of the survey appear in Figure 1 for the overall sample as well as countries that have 50 or more respondents. Overall, results show that the adoption rate of machine learning methods is 45%. Twenty-one percent of respondents indicate their company is exploring ML methods. Twenty percent of respondents indicate their company does not use ML methods.

Countries that are early adopters of ML methods include:

Israel (63% adopt ML)

Netherlands (57%)

United States (56%)

UK and Northern Ireland (54%)

Germany (54%)

Australia (53%)

France (52%)

China (52%)

Taiwan (51%)

Greece (49%)

Countries with the lowest adoption rate of ML methods include:

Nigeria (23% adopt ML)

Morocco (24%)

Egypt (31%)

Philippines (31%)

Argentina (32%)

Countries with the highest percent of companies exploring ML methods include:

Chile (36% are exploring ML methods)

Sweden (35%)

Malaysia (32%)

South Korea (31%)

Peru (29%)

Machine Learning Adoption Rates Around the World

Bob Hayes

A worldwide survey of data professionals showed that adoption of machine learning methods in their company is 45%. Twenty-one percent of survey respondents said their employer is exploring ML methods. ML adoption rates varied by country with Israel (63%), Netherlands (57%) and the United States (56%) showing the highest and Egypt (31%), Morocco (24%) and Nigeria (23%) showing the lowest adoption rate. ML adoption also varied by company size, with larger companies having higher adoption rates (61%) than medium (45%) and small (33%) companies.

Businesses are leveraging the power of machine learning methods to help them extract better quality information, increase productivity, reduce costs and extract more value from their data. As the amount of data continues to grow along with the processing power of technology, businesses will continue to incorporate ML into their business. Researchers have found different AI / ML adoption rates. In one study, adoption rate of ML Methods was 10%; in a 2020 study by McKinsey, adoption rate of AI was 50%. Still, another study found that 42% of companies were currently using AI and 40% of companies were planning on using AI in the next two years. Another 2020 study found that 59% of enterprises have machine learning initiatives either in production or at a proof-of-concept stage.

Current Analysis on Machine Learning Adoption

Kaggle conducted a worldwide survey in October 2020 of 20,036 data professionals (2020 Kaggle Machine Learning and Data Science Survey). The survey sample consisted of data professionals, including men (~79%) and women (~19%), from a variety of job titles (e.g., data scientist, business analyst, machine learning engineer, software developer) and company sizes. The survey asked a variety of questions, including “Does your current employer incorporate machine learning methods into their business?”

Figure 1. Machine Learning Adoption Rates across Countries. Click image to enlarge.

Figure 2. Adoption of ML Methods Across Company Size

Figures in:

Machine Learning Adoption by Company Size

We also looked at adoption rates by company size. Those results appear in Figure 2. Supporting prior studies, we found that larger companies have higher adoption rates about ML methods. The largest enterprise companies (10,000+ employees) reported ML adoption rates of 61%. The smallest companies (0-49 employees) reported adoption rates of 33%. Of the smallest companies, a little over a quarter of them (27%) indicate that they are exploring the use of ML methods.

Summary

Survey of data professionals showed that adoption rates of machine learning methods among businesses is 45%. About 21% of respondents indicated that their company is exploring machine learning methods with the hope of putting a model into production one day.

ML adoption rate varies by country and company size. Survey results reveal that early adopters come from large enterprise companies (adoption rate of 61%) and some countries including the United States, Israel, Netherlands and the UK and Northern Ireland.

Machine learning vendors, looking for inroads into businesses, could focus their marketing and sales efforts on small businesses as they have the highest percentage of companies who are exploring the use of ML methods.

SOURCE

http://businessoverbroadway.com/2021/02/01/machine-learning-adoption-rates-around-the-world/

Posted in Advanced Computing Platform, An executive's guide to AI, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Conference Coverage with Social Media on October 8, 2020| Leave a Comment »

Tweets & Retweets by @pharma_BI and @AVIVA1950 at #BioIT20, 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 in Boston

Tweets & Retweets by @pharma_BI and @AVIVA1950 at #BioIT20, 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 in Boston

Reporter: Aviva Lev-Ari, PhD, RN

Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN

Amazing conference ended at 2PM on October 8, 2020

e-Proceedings 19^th Annual Bio-IT World 2020 Conference, October 6-8, 2020 Boston

Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/26/19th-annual-bio-it-world-2020-conference-october-6-8-2020-in-boston/

Review Tweets and Retweets

Bio-IT World

and 2 others liked your Tweet

#BioIT20 Plenary Keynote: cutting innovative approach to #Science #Game On: How #AI, #CitizenScience #HumanComputation are facilitating the next leap forward in #Genomics and in #Biology may be in #PrecisionMedicine in the Future @pharma_BI @AVIVA1950 pic.twitter.com/L52qktkeYc

and

Retweeted your Tweet

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liked your Tweet

NIH Office of Data Science Strategy

@NIHDataScience

We’ve made progress with #FAIRData, but we still have a ways to go and our future is bright. #BioIT20 #NIHData

Aviva Lev-Ari

@AVIVA1950

#BioIT20

Driving Scientific Discovery with Data Digitization great ideas shared by moderator Timothy Gardner

@Riffyn

#CEO Inspiration from History Total Quality Implementation is key for BioScience Data #AI won’t solve the problem #Data #Quality will

Aviva Lev-Ari

@AVIVA1950

#BioIT20

out standing panel moderated by

@KevinDavies

#CRISPR Journal BBC Tagging system superior than Metadata efforts in BioScience

Rob Lalonde

@HPC_Cloud_Rob

My #BioIT20 talk, “#Bioinformatics in the #Cloud Age,” is tomorrow at 3:30pm. I discuss cloud migration trends in life sciences and #HPC. Join us! A panel with

@illumina

and

@cyrusbiotech

follows the talk.

datamanagepro

@datamanagepro

Oct 5

Have you registered for Bio-IT World yet? #BioIT20 #NetApp ntap.com/3ivbo3j

Jean Marois

@JeanMarois

Oct 5

My team is participating in Bio-IT World Virtual 2020, October 6-8. Join me! Use discount code 20NUA to save 20%! invt.io/1tdbae9s8lp

#BioIT20

I’m going to Bio-IT World 2020, Oct 6-8, from home! Its a virtual event. Join me!

My team is participating in Bio-IT World Virtual 2020, October 6-8. Join me! Use discount code 20NUA to save 20%! @bioitworld #BioIT20

invt.io

NIH Office of Data Science Strategy

@NIHDataScience

One of the challenges we face today: we need an algorithm that can search across the 36+ PB of Sequence Read Archive (SRA) data now in the cloud. Imagine what we could do! #BioIT20 #NIHdata #SRAdata

NCBI Staff

@NCBI

NCBI’s virtual #BioIT20 booth will open in 15 minutes. There, you can watch videos, grab some flyers and even speak with an expert! bio-itworld.pathable.co/organizations/ The booth will close at 4:15 PM, but we’ll be back tomorrow, Oct 7 and Thursday, Oct 8 at 9AM.

Bio-IT World

Welcome to Bio-IT World Virtual

bio-itworld.pathable.co

Show this thread

PERCAYAI

@percayai

Happening soon at #BioIT20: Join our faculty inventor Professor Rich Head’s invited talk “CompBio: An Augmented Intelligence System for Comprehensive Interpretation of Biological Data.”

Cindy Crowninshield

@C_Crowninshield

Join us at

Virtual Conference Plenary Keynote tomorrow Wed 10/7 to learn about AI, Citizen Science, and Gamification solving biomedical data problems and advancing #precisionmedicine! Register free here: lnkd.in/eZcNmYc #BioIT20

Wendy Anne Warr

@WendyAnneWarr

#bioit20 think I must have sent all my tweets to #bioitworld

Wendy Anne Warr

@WendyAnneWarr

This was a good discussion

Quote Tweet

Cambridge Innovation

@CIInstitute

· Oct 7

RT percayai: We’ve put together what’s sure to be a thought-provoking discussion group for #BioIT20 “Why Current Approaches Using #AI in #…

Joint Genome Inst.

@jgi

Oct 2

Our

@jgi

CIO Kjiersten Fagnan is part of the #BioIT20 Trends in the Trenches panel! Reserve your complimentary pass by Oct. 2 to hear her and others at the Oct. 6-8

conference: bit.ly/3nahMQL! #datascience #bioinformatics

@LBNLBioSci

@BerkeleyLab

@NERSC

PERCAYAI

@percayai

Oct 1

You won’t want to miss Dr. Philip Payne, the director of the Institute for Informatics

@WUSTLmed

, at #BioIT20. He will present “Accelerating Drug Discovery: In Silico Hypothesis Generation,” on Thursday, Oct. 8 at 10:40 a.m. EST. percayai.com/bioit2020 #AI #DrugRepurposing

Cambridge Innovation

@CIInstitute

RT VishakhaSharma_: Excited to speak and moderate a panel on Emerging #AI technologies bioitworld #BioIT20

Titian Software

@TitianSoftware

Oct 2

Meet Titian at #BioIT20 on 6-8th October and discover the latest research, science and solutions for exploring the world of precision medicine and the technologies that are powering it: bit.ly/2GjCj4B

Zogenix

@zogenix

#Zogenix is proud to be a sponsor of

@curedravet

‘s #DayofDravet Virtual Workshop! The opportunity to learn and connect is right around the corner. Pre-register by October 14th to attend this free event! bit.ly/3lZVZuv #Dravet

PERCAYAI

@percayai

23h

Thanks for joining us, Wendy! You’ve done a great job summing up key points from the discussion. #BioIT20

Aviva Lev-Ari

@AVIVA1950

Aviva Lev-Ari

@AVIVA1950

#NIHhealthInitiative #BioItWorld20

Out standing Plenary Keynote on #DataScience

@NIH

CONNECTED DATA ECOSYSTEM FAIR Foundable, Accessible, Interoperable, reusable

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e-Proceedings 2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET

e-Proceedings 2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET

Featuring Clinical, Scientific, Tech, AI and Venture Experts

https://worldmedicalinnovation.org/

7:50NOW PLAYING

2020 WMIF | Welcome

34 views1 hour ago

5:31NOW PLAYING

2020 WMIF | Disruptive Dozen #1

122 views1 day ago

3:27NOW PLAYING

2020 WMIF | Salute to Our Caregivers

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1:18NOW PLAYING

2021 World Medical Innovation Forum | Gene and Cell Therapy

564 views1 day ago

3:56NOW PLAYING

2020 WMIF | Disruptive Dozen #4

57 views2 days ago

SOURCE

https://www.youtube.com/channel/UCauKpbsS_hUqQaPp8EVGYOg

THIS IS THE EVENT I COVERED on 5/11/2020 BY INVITATION AS MEDIA for Mass General Brigham

From: “Coburn, Christopher Mark” <CMCOBURN@PARTNERS.ORG>

Date: Tuesday, May 12, 2020 at 6:48 AM

To: “Coburn, Christopher Mark” <CMCOBURN@PARTNERS.ORG>

Subject: REGISTRANT RECAP | World Medical Innovation Forum

Dear World Forum Attendee,

On behalf of Mass General Brigham CEO Anne Klibanski MD and Forum co-Chairs Gregg Meyer MD and Ravi Thadhani MD, many thanks for being among the nearly 11,000 registrants representing 93 countries, 46 states and 3200 organizations yesterday. A community was established around many pressing topics that will continue long into the future. We hope you have a chance to examine the attached survey results. There are several revealing items that should be the basis for ongoing discussion. We expect to be in touch regularly during the year. Among the plans is a “First Look” video series highlighting top Mass General Brigham Harvard faculty as well as emerging Harvard investigators. As promised, we wanted to also share visual Forum session summaries. You will be able to access the recordings on the Forum’s YouTube page . The first set will go up this morning

We hope you will join us for the 2021 Forum!

Thanks again, Chris

Mass General Brigham (formerly Partners Healthcare) is pleased to invite media to attend the World Medical Innovation Forum (WMIF) virtual event on Monday, May 11. Our day-long interactive web event features expert discussions of COVID-related infectious disease innovation and the pandemic’s impact on transforming medicine, plus insights on how care may be radically transformed post-COVID. The agenda features nearly 70 executive speakers from the healthcare industry, venture, start-ups, consumer health and the front lines of COVID care, including many of our Harvard Medical School-affiliated researchers and clinicians. The event replaces our annual in-person conference, which we plan to resume in 2021.

Aviva Lev-Ari, PhD, RN, Editor-in Chief, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston will cover the event in Real Time as MEDIA for our Coronavirus Portal

CORONAVIRUS, SARS-CoV-2 PORTAL @LPBI

http://lnkd.in/ePwTDxm

Launched on 3/14/2020

8:15 – 8:25 AM

Opening Remarks

Dr. Klibanski will welcome participants to the 2020 World Medical Innovation Forum, a global — and this year, virtual — gathering of more than 5,000 senior health care leaders. This annual event was established to respond to the intensifying transformation of health care and its impact on innovation. The Forum is rooted in the belief that no matter the magnitude of that change, the center of health care needs to be a shared, fundamental commitment to collaborative innovation – industry and academia working together to improve patient lives. No collaborative endeavor is more pressing than responding to the COVID-19 pandemic.

Introduction:
Scott Sperling, Co-President, Thomas H. Lee Partners; Chairman of the Board of Directors, Mass General Brigham

Introducing Anne Klibanski – Leadership at its best for breakthroughs in the entire system when return to normalcy

Anne Klibanski, MD, President & CEO, Mass General Brigham

Collaborative innovation between Industry and Hospitals and Government
Expediting innovations: Prophylactic, Diagnostics, research and care delivery
COVID caregivers contribution to this battle, patient experience and outcome

8:25 – 8:50 AM

Care in the Next 18 Months – Routine, Elective, Remote

Hospital chief executives reflect on how health care will evolve over the next 18 months in the face of COVID-19. What will routine health care look like? What about elective surgeries and other interventions? And will care-at-a-distance continue to be an essential component? Simply put, how will we provide manage, and pay for health care in a world forever changed by COVID-19?

Moderator:
Gregg Meyer, MD, Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor of Medicine, HMS

John Fernandez, President, Mass Eye and Ear and Mass General Brigham Ambulatory Care

Out patients decrease in volume now social distancing enabled by using parking lot as waiting rooms
Pre visit and post visit websites will become places of touch – patients accessing via website

Elizabeth Nabel, MD, President, Brigham Health; Professor of Medicine, HMS

Support to frontline care
Old normal will not be the new normal
Telehealth and digital health, work force, healthcare experience, improve access
lower medical expense
Patients were afraid
deferred cancer operation and treatment
Cath Lab less 50% occupied
Hospitals are safe and patients must come back for procedures
COVID-19 only 20% of all patients
ICU and OR Scheduling rethink procedure digital care delivers procedures
deploy workforce work across repurposed units hybrids, talent acquisition new strategy
COVID-19 will have distinct areas
BWH – Patient-Nurse-Doctor relations in healing Healthcare team became the Family of the Patients

Peter Slavin, MD, President, MGH; Professor, Health Care Policy, HMS

Reemerging more complicated
In patients and Out patient realigned with care for COVID-19
Telemedicine 85% of outpatients visits at MGH
virtual care will dominate the future of care
disadvantaged populations suffered more in the pandemic Communities in Chelsea and Revere household received kits social determinants of illness

8:50 – 9:15 AM

COVID-19: Technology Solutions Now and in the Future

Experts leading large teams at the epicenter of the coronavirus outbreak discuss how technology is shaping the pandemic response today and in the coming years. What technology categories are most important? What tools are healthcare organizations, biopharmaceutical companies, and other organizations leveraging to battle this crisis? How will those tools evolve? And, importantly, how can technology inform the medical response to future pandemics? What were the biggest technology surprises in the current response?

Moderator:
Alice Park, Senior Writer, Time

Stephane Bancel, CEO, Moderna

mRNA synthetic RNA of Spike protein injected to stir immune response
Phase II working with FDA starting Phase III early Summer
15 mcg dose available in 2020
using own capital to invest to scale up manufacturing no help from Gov’t Grant for clinical trial not for manufacturing

Paul Biddinger, MD, Medical Director for Emergency Preparedness, MGH; Associate Professor of Emergency Medicine, HMS

Sharing information across the system aggregate data technologies
ML as Guidance in resource coordination

David Kaufman, MD, PhD, Head of Translational Development, Bill & Melinda Gates Medical Research Institute

drug development, clinical operations remote monitoring
repurpose compounds usinf libraries
scalability and Global vaccine cheap and available globally
complexity is in coordinations – toolset biology tool RNA mapping viral screening primaru cells and organoids
Outcomes: Aging and co-morbidities
Discovery effort using tools infrastructure maintained between pandemics

Rochelle Walensky, MD, Chief, Infectious Disease, Steve and Deborah Gorlin MGH Research Scholar, MGH; Professor of Medicine, HMS

shared photos important for Public health, using iPhone distribution Demedicalize Testic – not only at clinics but at many placed contact tracing and diagnosis in 24 hours – iPhone is invaluable GPS capability – privacy issues
detect patients with high risk and existing infection monitoring
Public Health – Thermometer given to Patients – data collected centrally any spike and pulse oximeter given to home – remote
Anxiety in opening the economy requires a bit of giving up on privacy
TeleHealth and monitoring remotely
Pharmacy and workplace as points to start Testing vs Order and a nurse call

9:15 – 9:40 AM

Digital Health Becomes a Pillar: Tools, Payment, Data

Deployed in the crucible of the coronavirus pandemic, digital health has now become an essential pillar in the delivery of care. Why is that significant? How and why did it happen? What are the essential tools and components? How is the electronic health record and other health data contributing to this digital movement?

Are there novel use cases for telehealth that arose during the first phase of the COVID-19 pandemic? How can digital technologies help enable a full return to work. Thinking ahead to the fall and a possible second wave, are there things we should be doing today to ensure this technology to better detect and profile a resurgence and enhance the patient benefit.

Moderator:
David Louis, MD, Pathologist-in-Chief, MGH; Benjamin Castleman Professor of Pathology, HMS

DIgitsl technologies – boostong and innovating
upscale activity
risk of upscaling on Providers
Adaptations of innovation

Alistair Erskine, MD, Chief Digital Health Officer, Mass General Brigham

Adam Landman, MD, VP, Chief Information and Digital Innovation Officer, BH; Associate Professor of Emergency Medicine, HMS

COVID-19 call center across Partners, Chat bots automated screening tools, Microsoft assisted 60,000 users of chat bots triaging by screening calls of the Hotline
TeleHealth transformation may be lost due to reimbursement which may not be reimburse after the emergency is over Insurers to incentivize use of of TeleHealth
In person care: Redesign and how to provide In care for the staff and for the Patients

Brooke LeVasseur, CEO, AristaMD

Access problem due to care shortage of specialty care
technology better allocate resources
Industry and Hospital Institutions populations they serve
innovations needs a sustainable economic model for reimbursement
Inequity issues How Telehealth can benefit all of Society, potential for future solutions

Lee Schwamm, MD, Director, Center for TeleHealth and Exec Vice Chair, Neurology, MGH; Vice President, Virtual Care/Digital Health, Mass General Brigham; Professor, Neurology, HMS

Surge capabilities
generate insight
Research and Innovation needs embedding in the enterprise
technical gap in maintenance
supply chain disrupted

9:40 – 9:45 AM

BREAK

9:45 – 10:05 AM

FIRESIDE CHAT

Bayer Pharma Reflections on Innovation: Creating, Collaborating, and Accelerating Discovery During and After a Pandemic

Dr. Moeller will reflect on how Bayer is weathering the organizational challenges posed by the COVID-19 pandemic. How does a global pharmaceutical company continue to drive drug development when its labs are shut down? What are the critical elements needed to keep the engines of innovation firing even in the face of a global public health crisis? How does a global r&d enterprise plan for an uncertain fall 2020 given a potential return of the virus.

Introduction:
John Fish, CEO, Suffolk; Chairman of Board Trustees, Brigham Health

COPD

Moderator:
Janet Wu, Bloomberg

Joerg Moeller, MD, PhD, Head of Research & Development, Pharmaceuticals Division, Bayer AG

led team of 9 products
Unprecedented is COVID-19: effect on work, travel, life
Anti-Malaria vs COVID-19: In China testing early chloroquine approved for RA and anti Malaria Government in China experimental and Bayer supports Clinical Trials by Bill & Melinda Foundation
In 8 weeks most Scientist work from home – amazed what was accomplished by 80% of Bayer working from home
production is kept ongoing anti-infective for Pneumonia
focus on most critical and keep experiment critical and push out studies run Globally – No pre-maturely study was interrupted completely
Great collaboration Flexibility with regulatory agencies in Europe and with FDA – levels not seen before
R&D in Pharma – when out different point than when we started: Opportunities- Compound libraries OPEN after the COVID Pandemic, speed of decision making, team spirit outstanding – levels not seen before
Partnerships: Bayer testing machines and ventilators shared, accelerate mechanisms for new drug development
evidence for repurposing drugs: Chloroquine
Solidarity – everyone are in it TOGETHER, keep that after the Pandemic is over – levels not seen before

10:05 – 10:30 AM

The Patient Experience During the Pandemic

The coronavirus outbreak is not only testing health care staff and resources, it is also having an overwhelming impact on patients. This panel will focus on the approach and technologies providers are using to address the patient experience along the continuum of care.

Moderator:
Thomas Sequist, MD, Chief Patient Experience and Equity Officer, Mass General Brigham; Professor of Medicine and Health Care Policy, HMS

Anjali Kataria, CEO, Mytonomy

Video overcome illiteracy and provide personal engagement without the negative
Home health will be the shift – a human component will not go away – sensor technology in car, bathroom
COVID-19 accelerated user adoption of Telehealth
Digital technologies as an equailizer Hispanic patients consumed for information with the new technologies

Daniel Kuritzkes, MD, Chief, Division of Infectious Diseases, BH; Harriet Ryan Albee Professor of Medicine, HMS

conserve PPE impacted Physicians ability to see Patients, Nurses meet patients vs Physicians that delivered care remotely – laying on hands was missing in the care
Masks will not come off but in a while, can’t allow the infection to surge and curtail hospitals from functioning, use mask for the foreseable future

Peter Lee, PhD, Corporate Vice President, Microsoft Research and Incubation

Interactive Chat bots 1 out of 500 hospitals around the Globe adopted the Chat Bot for Patient Intake
Scaling telemetry with feedback loop
iPad at bedside, platform orchestration, new workflows for COVID-19 patients in the backend guiding Patients in the Process was new infrastructure was in the front line
preparing for a game change in Medicine: Patients demanding new experience
Historical context for physicians contribution to care and bridge the digital divide

Jag Singh, MD, PhD, Cardiologist & Founding Director, Resynchronization and Advanced Cardiac Therapeutics Program, MGH; Professor of Medicine, HMS

Isolation is unbearable
Predictive analytics
no going back to before Pandemic
COVID-19 only severe go to hospital
Human contact enhanced interaction with families and Docs

10:30 – 10:55 AM

The Role of AI and Big Data in Fighting COVID-19 and the Next Global Crisis – Successes and Aspirations

AI is a key weapon used to fight COVID-19. What are the biggest successes so far? Which applications show the most promise for the future? Can it help a return to work? Can AI help predict and even prevent the next global health care crisis?

Moderator:
Alice Park, Senior Writer, Time

Mike Devoy, MD, EVP, Medical Affairs & Pharmacovigilance and CMO, Bayer AG

AI allows speeding up Genome of Spike Proteins sequencing
Partnership with Academia help focus effort
openness and willingness to collaborate and take risk in Therapeutics

Karen DeSalvo, MD, Chief Health Officer, Google Health

Partnership with Apple on Contact Tracing System – BLE – only for Health applications
Public Health as driver as consumer Privacy preserving
Individual level data collection for AI applications, privacy giving up for public good
Trust component – in sharing data

Keith Dreyer, DO, PhD, Chief Data Science Officer, Mass General Brigham; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS

COVID allowed data on contact tracing
AI in image capturing for Public health – target Imaging use data to be equivalent to Human Testing at Home va in ER 1 in 10, 000 vs all populations
Data to AI application SW providers are stewards Open source , no conflict of interest and no discussion on profits
Each country will have own lessens

10:55 – 11:20 AM

Designing for Infection Prevention: Innovation and Investment in Personal Protective Equipment and Facility Design

As with many pathogens, prevention is the best defense against SARS-CoV2, the virus that causes COVID-19. Panelists will discuss the insights, design strategies, technologies, and practices that are emerging to guard against infection and how those innovations are being applied to protect health care providers and their patients.
Based on what was learned during the spring of 2020, are there specific changes that will lessen morbidity and mortality in a potential a second wave?

Moderator:
Erica Shenoy, MD, PhD, Associate Chief, Infection Control Unit, MGH; Assistant Professor, HMS

Shelly Anderson, SVP, Strategic Initiatives and Partnerships, & Chief Strategy Officer, BH

How to establish the New normal
Surveillence for new sources of infection
Operations under uncertainty
learned to be effective with data monitoring, training, facility adaptation to new roles
Investments in new materials to stabilize the supply chain: Additional suppliers,
Extend internal supply work with R&D on alternative materials

Michele Holcomb, PhD, EVP, Strategy and Corporate Development, Cardinal Health

Optimize toward lower cost vs availability of supply
Diverting supply chain to manufacturing not in PPE business

Guillermo Tearney, MD, PhD, Remondi Family Endowed MGH Research Institute Chair, Mike and Sue Hazard MGH Research Scholar, MGH; Professor, Pathology, HMS

3D Printing innovations for filtration capacity of particles, respirators decontaminated, prevention of patient transmission
Negative pressure applied on materials as second line of protection beyond PPE
CPAP to be used
weaning from Ventilators to CPAP
Environment to be protected from air born pathogens

Teresa Wilson, Director/Architect, Colliers Project Leaders

Physical Design of the facility and rooms – use design to minimize Hospital infections principals of location of clean vs dirty functions
room kept cleaned, how long it takes to clean, where is the sink, hands free, modular construction plug & play design of rooms functions

11:20 – 11:25 AM

BREAK

11:25 – 11:45 AM

FIRESIDE CHAT

Preparing for Fall 2020 and Beyond: Production, Innovation, Optimization

How does a global medical technology and life sciences company respond to the health challenges posed by COVID-19? Mr. Murphy will reflect on how his organization is working to meet the unprecedented demand for life-saving medical equipment for diagnosing, treating, and managing coronavirus patients. How does a large manufacturer make adjustments to FDA regulated products and supply chains in time to help lessen the impact of a second wave of COVID-19 infections.

Introduction:
Jonathan Kraft, President, The Kraft Group; Chair, Mass General Hospital Board of Trustees

90 countries around the Globe – collaborative innovations partnership with GE Health – all assets around the World
Academic with GE Health AI, Diagnostics, data set for ML for Health care

Moderator:
Timothy Ferris, MD, CEO, MGPO; Professor, HMS

Kieran Murphy, CEO, GE Healthcare

Partnership GE Health & MGH
COVID-19 Innovations and Customers needs: Ventilators and
ICU Cloud application with Microsoft to save PPE and Labor, monitor several ICU rooms at once by technology
Quadruple the production and enter new contracts, crisis exposed weaknesses in supply chain of many products
Shortage of PPE was not expected, flexibility and trusted relations with GE Health Suppliers
CT in a BOX – 42 Slices in a container – no exposure to radiation in prefabricated rooms in field hospital requiring no contact with clinicians and rapid response
Command control center with John Hopkins University
Manufacturing facilities in China communicate the situation of the business and the customers needs buyers in the Health care industry
Future for Biotech industry: Modular systems deploy rapidly, test vaccine, SPEED is everything productivity & Speed
Productivity will increase collaboration and speed like partnership with FORD and MIcrosoft

11:45 AM – 12:10 PM

Big Tech and Digital Health

Tech giants are dedicating their vast resources to aid in the global response to the coronavirus. This panel will highlight how the big data and computational power of major tech companies is being deployed to help contain the current pandemic through new technologies and services, enable return to work, and how it could help prevent future ones.

Moderator:
Natasha Singer, Reporter, New York Times

Amanda Goltz, Principal, Business Development, Alexa Health & Wellness, Amazon

Michael Mina, MD, PhD, Associate Medical Director, Molecular Virology, BH; Assistant Professor, Epidemiology, Immunology and Infectious Diseases, Harvard Chan School

Limitations on Viral Testing
Shortage of Swabs for testing
Tech giant: Amazon, Walmart – global reach in supply chain
new collaborations formed on super charge
Antigen test for home administration consumerization of the Testing
Walmart can be positioned for blood tests
Not only Physicians can order tests
Microsoft and Amazon can help in interpretation of the Test using Alexa

Marcus Osborne, VP, Walmart Health, Walmart

Jim Weinstein, MD, SVP, Microsoft

12:10 – 12:35 PM

LUNCH BREAK

12:35 – 12:55PM

FIRESIDE CHAT

Insights on Pandemics and Health Care from the National Security Community

General Alexander, a renowned expert on national security as well as pandemics and health care, will reflect on how AI can help identify and predict future global disease outbreaks and enable fully reopening commerce. He will also discuss what health care systems can learn from the response to COVID-19 to ensure preparedness for the next infectious disease challenge.

Moderator:
Gregg Meyer, MD, Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor of Medicine, HMS

General (Ret) Keith Alexander, Co-CEO, IronNet Cybersecurity

12:55 – 1:20 PM

Calibrating Innovation Opportunity and Urgency: Medical and Social

The social and medical needs of patients are deeply intertwined, yet there are significant gaps in the tools and technologies being developed to help address those needs. These are especially apparent in the non-uniform impact of COVID-19. Harnessing opportunities, particularly for patients whose needs fall into the low medical complexity/high social complexity category — a group often overlooked by health care innovators.

Moderator:
Natasha Singer, Reporter, New York Times

Giles Boland, MD, Chair, Department of Radiology, BH; Philip H. Cook Professor of Radiology, HMS

Boston Hope: 1400 patients were treated at Boston Convention Center, 700 COVID -19 patients and 700 post acute after release from ICUs
Policy makers to address social determinants of Health

Amit Phadnis, Chief Digital Officer and GE Company Officer, GE Healthcare

Crisis will go away the innovations will stay and develop
Population Health to benefit from iPhone in Africa and in India mapping hotspots in populations
Multi channels TV, Phones and other devices – social disparities – no app to address social inequality

Krishna Yeshwant, MD, General Partner, GV; Instructor in Medicine, BH

communities most affected by social determinants of Health like in Chelsea in MA, a hotspot for COVID-19
Google Ventures – social issues are most complex invest in underprivileged

1:20 – 1:45 PM

FDA Role in Managing Crisis and Anticipating the Next

The FDA and other regulatory bodies have played a key role in managing the coronavirus pandemic. How will the agency’s priorities shift in the coming months as community transmission (ideally) slows? What is the FDA’s role in return to work? What is the FDA doing to anticipate future health crises? How will these drive new tools and effect that rate of innovation?

Moderator:
Ravi Thadhani, MD, CAO, Mass General Brigham; Professor of Medicine and Faculty Dean for Academic Programs, HMS

Amy Abernethy, MD, PhD, Principal Deputy Commissioner & Acting CIO, FDA

Future – common tools, more efficient studies study protocols and study design evaluation
Learned what need to be put in place to move fast learn what is not in place
post pandemic regulatories lessons for being ready for the next one

Lindsey Baden, MD, Director, Clinical Research, Division of Infectious Diseases, BH; Associate Professor, HMS

Identify diagnostics for clinical definition of a virus unknown
treatment to be developed
Sick patients in need for treatment, researchers and clinicians need the best available FDA and the hospitals are flexible in responding
Spread globally like a respiratory virus
IRB – fast than ever before FDA and Pharma, DSMB – speed

1:45– 2:05 PM

FIRESIDE CHAT

Keeping Priority on the Biggest Diseases

Biogen CEO Michel Vounatsos will discuss how Biogen is tackling some of society’s most devastating neurological and neurodegenerative disorders, and share his perspective on the impact the global COVID-19 pandemic is having on the biopharmaceutical industry.

Moderator:
Jean-François Formela, MD, Partner, Atlas Venture

Testing programs – lack of government cooordination

Michel Vounatsos, CEO, Biogen

Venture community supportive
to be on the safe side
employees tested every evenings to prevent rebound of the pandemic
Pandemic is acceleration progress that was only dreamt about
Opportunities in technologies new drugs,
Biogen will lead the new model
ALS – rare genetic expression Phase I encouraging
Neuro-immunology – MS phase III Parkinson drug
Lessons from COVID-19: Delay in clinical trials because Patients are fearing Hospital admission – Stroke patient did not go to Hospital
Biogen is joining the fight against COVID
Neuroimmunology is the strength – remain focus

2:05 – 2:30 PM

Building the Plane While Flying: The Experience of Real-Time Innovation from the Front Line

The COVID-19 crisis has required continuous, real time innovation, impacting the way care is delivered on the front lines and across care continuum. This panel will present the perspective, innovations and experiences of care givers interacting directly with patients across the continuum of care – acute, post-acute, rehab and home care.

Moderator:
Ann Prestipino, SVP; Incident Commander, MGH; Teaching Associate, HMS

coming out of crisis
the New normal will be diferent

Theresa Gallivan, RN, Associate Chief Nurse, MGH

Ambulatory procedures
700 nurses were deployed
164 ICU beds increase of 90%
Health care demand will change in the future
focussed problem alarms from ventilators were not coordinated till biomed engineers arrives to device a solution

Karen Reilly, DNP, RN, Associate Chief Nursing Officer, Critical Care, Cardiovascular and Surgical Services, BH

Collaborate and move forward
Interdisciplinary team: Physical therapy help quickly
tech to communicate with families
Ready – I wish I had information to stay ahead of the curve
New normal ability to expand and contract

Ross Zafonte, DO, SVP, Research Education and Medical Affairs, SRN; Earle P. and Ida S. Charlton Professor of Physical Medicine and Rehabilitation, HMS

Rehabilitation in Cambridge Spaulding Brighton
Off loading to rehab from other units
Flexibility MGH Brigham – learn to be a new organization
Hotspots optimal mapping
Right person at right challenge
Stay ready for catastrophies
Telecare and Tele rehabilitation – greater benefit on TeleHealth or not who will not benefit from Rehab

2:30 – 2:55 PM

CEO Roundtable: Will the Innovation Model Remain as It Was

As we envision a post-COVID-19 world, how will the model for biomedical innovation change? What lessons have been learned? Was this pandemic a once-in-a-lifetime event or should organizations begin to weave pandemic planning into their business and operations strategies? Panelists will discuss these and other related questions.

Moderator:
Janet Wu, Bloomberg

Mike Mahoney, CEO, Boston Scientific

China 6% of Sales
Employees – 148 Counties
support hospitals – 57% of volume
Resilience for liquidity Variable cost needed be removes partially
How will the company come out stronger
Innovations by business model innovations – Remote physicians in Japan by European experts in OR
Next week 10% of Product management and Quality are priority to come back
working remotely works very well except for R&S who needs Labs

Bernd Montag, PhD, CEO, Siemens Healthineers

Keep present business and the emerging needs for technologies
Serology Test
Antibody Test genomic testing
Company is Global but Health care is local

2:55 – 3:05 PM

BREAK

3:05 – 3:30 PM

Emergency and Urgent Care: How COVID-19 Vulnerabilities and Solutions Will Change the Model

How are the roles of emergency medicine and urgent care changing in light of the COVID-19 pandemic? Panelists will discuss this topic as well as how current and anticipated new technologies can aid in the delivery of community, urgent, and emergency care now and in the future.

Given a false negative at the point of care has consequences well beyond the patient being treated, does this change what can be offered in the various patient care settings?

Moderator:
Ron Walls, MD, EVP and Chief Operating Officer, BH; Neskey Family Professor of Emergency Medicine, HMS

Troyen Brennan, MD, EVP and CMO, CVS Health

Labs – Quest Diagnostics
Point of care – Tests will move to Home will replace Labs
Pandemic heated hard people of color and comorbidities

David Brown, MD, Chair, Department of Emergency Medicine, MGH; MGH Trustees Professor of Emergency Medicine, HMS

Tele Urgent care
EMS Providers using TeleHealth
Scaled up capability needed administered by Governmental agency
new surges of some disease after Re-opening
Sensitivity of test for ill patient
Demand for Urgent Care will decline higher acuity will increase

Julie Lankiewicz, Head, Clinical Affairs & Health Economics Outcomes Research, Bose Health

Management of care with VRE other microbial agents
Vulnerable populations EKG between patients no more
mitigation of care – Brand new prescriptions for Anxiety and burnout
Digital solution to replace medications – audio content to avoid pharmacology by other methods of relaxation
Herd immunity – Digital transformation

Michael VanRooyen, MD, Chairman, Department of Emergency Medicine, BH; Director, Humanitarian Initiative, Harvard University; Professor, HMS

Separate Patients from Providers
Infection threat – Intubation – Tent for airsolize – trap air in the hood
manage Emergence Health OUT side of EM at Hospital
Rapid testing will continue to be central in Emergency Care

3:30 – 3:55 PM

Accelerating Diagnostics – Maintaining the Priority: Lab, Home and Digital

COVID-19 diagnostics, a linchpin in controlling viral spread — what caused testing in the U.S. to fall so far behind and how can those missteps be prevented in the future? How do the diagnostics industry, and academic medicine, develop the tests that enable group activities including businesses sports, and community? What is the profile of diagnostic tests coming online in the coming months and into next year? What lessons can be learned to guide the global health community in future disease outbreaks? Given the biological complexity, required performance standards, and immense volume is a simple DTC assays possible on a greatly accelerated timeline.

Moderator:
Jeffrey Golden, MD, Chair, Department of Pathology, BH; Ramzi S. Cotran Professor of Pathology, HMS

James Brink, MD, Chief, Department of Radiology, MGH; Juan M. Taveras Professor of Radiology, HMS

social determinant of care – communities not able to social distance, multiple languages
Radiology: Rapid evolution of pandemic
MGB – Standardizations

John Iafrate, MD, PhD, Vice Chair, Academic Affairs, MGH; Professor, Pathology, HMS

Ability for Rapid testing was not in existence in the US
CDC Test deployed
BD and Roche diagnostics will
recipients and donors of antibodies

Celine Roger-Dalbert, VP Diagnostic Assays R&D – Integrated Diagnostic Solutions, BD Life Sciences

Telemedicine collection of samples outside the hospital
Testing if a patient had – serology – antibody – past exposure after day 14
Testing if a patient has – PCR after 10 days the virus is not infectious but it is present
antigen detection testing
molecular test

Matt Sause, President and CEO, Roche Diagnostics Corporation

Serology – more people become infected
active infection
Partnership between FDA and the manufactures
In the US scaling – infrastructure in place is a must

3:55 – 4:15 PM

FIRESIDE CHAT

Return to Work: Understanding the Technologies and Strategies

Diagnostic testing is a linchpin of the worldwide response to the coronavirus. How does a global leader pivot to develop molecular diagnostics for a novel global pathogen? How does it scale, including managing international supply chains, to provide unprecedented levels of products and services. What are the expectations for return to work and a possible disease spike in fall 2020 or beyond. How will the diagnostics industry be permanently changed.

Moderator:
Peter Markell, EVP, Finance and Administration, CFO & Treasurer, Mass General Brigham

Marc Casper, Chairman, President and CEO, Thermo Fisher Scientific

Re-opening the economy requires Testing for certification of health
Testing bringing confidence
PCR – have or have not viral proteins: 5Millions a week, June 10 million tests
antibody testing will also become available in massive scale
Supply chain, more preparedness, robustness of the supply chain
Buying supply in China vs US based
stockpiling by governments not only at the Hospital level vs JIT shocks to the system
Work from home – productivity is good, work from home not ideal environment
Transportation and elevators – social distancing – impossible
Global change enormous Telemedicine ramp up Academic center Telemedicine will prevail
more resilient Health care system dialogue and communications across countries technology will play a role it will improve Health care every where

4:15 – 4:40 PM

Digital Therapeutics: Current and Future Opportunities

Digital therapeutics (DTx) represents an emerging class of therapies that is poised for significant growth. Yet already, these software-driven, evidence-based tools for the prevention, management, and/or treatment of disease are already changing patients’ lives. This panel will address how existing DTx are having an early impact — in the COVID-19 pandemic and — and where current development efforts are headed in the coming years especially if there is a aggressive return of the virus in the fall 2020 or later.

Moderator:
Hadine Joffe, MD, Vice Chair for Research, Department of Psychiatry, Executive Director, Mary Horrigan Connors Center for Women’s Health and Gender Biology, BH; Paula A. Johnson Professor, Women’s Health, HMS

Priya Abani, CEO, AliveCor

Medical grade EKG devices
Telemedicine on the rise

Julia Hu, CEO, Lark Health

AI 24×7 counseling data streaming in data
TeleHealth
VirtualHealth Provider – working hard to scale
Patients @Home work at their schedule 9PM – midnight text messaging
70% in employment reported stress experienced by employees

Dawn Sugarman, PhD, Assistant Psychologist, Division of Alcohol, Drugs, and Addiction, McLean; Assistant Professor, Psychiatry, HMS

Opioid & substance abuse
Treatment gap for women – gender specific Programs online gender specific treatment

4:40 – 5:05 PM

Investing During and After the Coronavirus Crisis

The investment environment in life sciences and health care overall was at record levels for most of the last decade. What will this environment look like in the wake of the COVID-19 pandemic – especially over the near to mid-term? Will investor priorities and enthusiasm shift? What is the investor role in developing new coronavisurs tests, vaccines, and therapeutics?

Moderator:
Roger Kitterman, VP, Venture and Managing Partner, Partners Innovation Fund, Mass General Brigham

Jan Garfinkle, Founder & Manager Partner, Arboretum Ventures

Can you close a deal with out meeting management team
Known funds will prevail vs new funds Parma adjacencies vs medical devices
Telehealth is of interest GI, Cardiovascular
Mental health with TeleHealth

Phillip Gross, Managing Director, Adage Capital Management

Clinical Trial issues
Inflating value of Biotech because therapeutic related to COVID gives a boost
90 programs in clinical trials on Vaccine

Christopher Viehbacher, Managing Partner, Gurnet Point Capital

Health care was great investment because prople will get sick.
deal making switch to zoom meeting, no site visit, banking is adapting
relationship with people you do not know will be very hard
early stage if the cloud exist
Medical profession: Healthcare system is hurting revenue loss new technologies
clinical trials will be changing like for COVID
Sharing data will accelerate science

5:05 – 5:10 PM

Closing Remarks

Gregg Meyer, MD, Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor of Medicine, HMS

Ravi Thadhani, MD, CAO, Mass General Brigham; Professor of Medicine and Faculty Dean for Academic Programs, HMS

Posted in AI-assisted Cardiac MRI, An executive's guide to AI, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial intelligence applications for cardiology, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Deep Learning in Pathology on January 30, 2020| Leave a Comment »

Artificial Intelligence in Medicine – Part 3 in https://www.amazon.com/dp/B08385KF87

Artificial Intelligence in Medicine – Part 3: in Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology

Updated on 2/10/2020

Eric Topol

@EricTopol

Feb 8, 2020

There have only been 5 randomized clinical trials of #AI in medicine to date. Here’s the summary: 4 in gastroenterology (2 @LancetGastroHep, 2 @Gut_BMJ) 1 in ophthalmology (@EClinicalMed) All were conducted in China (None in radiology, pathology, dermatology or other specialties)

Eric Topol

@EricTopol

Following

physician-scientist, author, editor. My new book is #DeepMedicine drerictopol.com

The Lancet Gastroenterology & Hepatology

@LancetGastroHep

The Lancet Gastroenterology & Hepatology publishes high-quality peer-reviewed research and reviews, comment, and news #gastroenterology #hepatology. IF=12.856

Gut Journal

@Gut_BMJ

Leading international journal in gastroenterology with an established reputation for publishing 1st class research. Find us on Facebook: facebook.com/Gut.BMJ

EClinicalMedicine – Published by The Lancet

@EClinicalMed

A new open access clinical journal, published by

@TheLancet

, influencing clinical practice and strengthening health systems

Eric Topol

@EricTopol

Replying to

@EricTopol

@LancetGastroHep and 2 others

While there are now hundreds of in silico, retrospective dataset reports, the number of prospective (non-randomized) trials in a real clinical environment testing #AI performance is limited. I only know of 11. Let me know if I’m missing any.

Curators: Stephen J. Williams, PhD, Dror Nir, PhD and Aviva Lev-Ari, PhD, RN

Series B: Frontiers in Genomics Research

Series Content Consultant:

Larry H. Bernstein, MD, FCAP, Emeritus CSO, LPBI Group

Volume Content Consultant:

Prof. Marcus W. Feldman

https://www.youtube.com/watch?v=aT-Jb0lKVT8

BURNET C. AND MILDRED FINLEY WOHLFORD PROFESSOR IN THE SCHOOL OF HUMANITIES AND SCIENCES

Stanford University, Co-Director, Center for Computational, Evolutionary and Human Genetics (2012 – Present)

Latest in Genomics Methodologies for Therapeutics:

Gene Editing, NGS & BioInformatics,

Simulations and the Genome Ontology

2019

Volume Two

https://www.amazon.com/dp/B08385KF87

Product details

File Size:3138 KB
Print Length:217 pages
Publisher:Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston; 1 edition (December 28, 2019)
Publication Date:December 28, 2019
Sold by:Amazon Digital Services LLC
Language:English
ASIN:B08385KF87
Text-to-Speech: Enabled
X-Ray:

Not Enabled

Word Wise:Not Enabled
Lending:Enabled
Enhanced Typesetting:Enabled

Prof. Marcus W. Feldman, PhD, Editor

Prof. Stephen J. Williams, PhD, Editor

and

Aviva Lev-Ari, PhD, RN, Editor

Introduction to Part 3: AI in Medicine – Voice of Aviva Lev-Ari & Professor Williams

There is a current consensus that of all specialties in Medicine, Artificial Intelligence technologies will benefit the most the specialty of Radiology.

What AI can do

Of course, there is still a lot AI can do for radiologists. Soonmee Cha, MD, neuroradiologist, has served as a program director at the University of California San Francisco since 2012 and currently oversees 100 radiology trainees, said at RSNA 2019 in Chicago

“we can see a future where AI is improving image quality, decreasing acquisition times, eliminating artifacts, improving patient communication and even decreasing radiation dose.

“If AI can detect when machines are being set up incorrectly and alert us, it’s a win for us and for patients,” she said.

https://www.aiin.healthcare/topics/medical-imaging/rsna-ai-imaging-healthcare-costs-radiology-trainees?utm_source=newsletter&utm_medium=ai_news

Radiology societies team up for new statement on ethics of AI

Michael Walter | October 01, 2019 | Artificial Intelligence

Numerous imaging societies, including the American College of Radiology (ACR) and RSNA, have published a new statement on the ethical use of AI in radiology.

The European Society of Radiology, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics (EuSoMII), Canadian Association of Radiologists and American Association of Physicists in Medicine all also co-authored the statement which is focused on three key areas of AI development: data, algorithms and practice. A condensed summary was shared in the Journal of the American College of Radiology, Radiology, Insights into Imaging and the Canadian Association of Radiologists Journal.

“Radiologists remain ultimately responsible for patient care and will need to acquire new skills to do their best for patients in the new AI ecosystem,” J. Raymond Geis, MD, ACR Data Science Institute senior scientist and one of the document’s leading contributors, said in a prepared statement. “The radiology community needs an ethical framework to help steer technological development, influence how different stakeholders respond to and use AI, and implement these tools to make the best decisions for—and increasingly with—patients.”

“The application of AI tools in radiological practice lies in the hand of the radiologists, which also means that they have to be well-informed not only about the advantages they can offer to improve their services to patients, but also about the potential risks and pitfalls that might occur when implementing them,” Erik R. Ranschaert, MD, PhD, president of EuSoMII. “This paper is therefore an excellent basis to improve their awareness about the potential issues that might arise, and should stimulate them in thinking proactively on how to answer the existing questions.”

Back in September, the Royal Australian and New Zealand College of Radiologists (RANZCR) published its own guidelines on the ethical application of AI in healthcare. The document, “Ethical Principles for Artificial Intelligence in Medicine,” is available on the RANZCR website.

https://www.radiologybusiness.com/topics/artificial-intelligence/radiology-societies-ethics-ai

Selective examples of applications of AI in the specialty of Radiology include the following:

RSNA 2019, the world’s largest radiology conference, kicks off at Chicago’s McCormick Place on Sunday, Dec. 1, 2019, and promises to include more AI content than ever before. There will be an expanded AI Showcase this year, giving attendees access to more than 100 vendors in one location.

“Artificial Intelligence and Precision Education: How AI Can Revolutionize Training in Radiology” | Monday, Dec. 2 | 8:30 – 10 a.m. | Room: E450A
“Learning AI from the Experts: Becoming an AI Leader in Global Radiology (Without Needing a Computer Science Degree)” | Tuesday, Dec. 3 | 4:30-6 p.m. | Room: S406B
“Deep Learning in Radiology: How Do We Do It?” | Wednesday, Dec. 4 | 8:30-10 a.m. | Room: S406B

https://www.aiin.healthcare/topics/medical-imaging/rsna-2019-preview-3-ai-sessions-radiology-imaging?utm_source=newsletter&utm_medium=ai_news

Interview with George Shih, MD, a radiologist at Weill Cornell Medicine and NewYork-Presbyterian and the co-founder of the healthcare startup MD.ai

An academic gold rush, where people are working to apply the latest AI techniques to both existing problems and brand new problems, and it’s all been really great for the field of radiology.

We’re also holding another machine learning competition this year hosted on Kaggle. In previous years, we’ve annotated existing public data that was used for our competition, but this year, we were actually able to acquire high-quality data—more than 25,000 CT examinations that nobody has used or seen before—from four different institutions. The top 10 winning algorithms will also be made public to anyone in the world, which is an amazing way to advance the use of AI in radiology. I think that’s one of the biggest contributions RSNA is making to the academic community this year.

The other exciting part is that our new and improved AI Showcase will include more vendors—more than 100—than any previous year, which shows just how much the market continues to focus on these technologies.

https://www.aiin.healthcare/topics/medical-imaging/radiologist-rsna-2019-ai-radiology-imaging?utm_source=newsletter&utm_medium=ai_news

AI model could help radiologists diagnose lung cancer

Michael Walter | November 27, 2019 | Medical Imaging

https://www.aiin.healthcare/topics/medical-imaging/ai-model-radiologists-diagnose-lung-cancer-imaging

AI a hot topic for radiology researchers in 2019

Michael Walter | November 26, 2019 | Medical Imaging

https://www.aiin.healthcare/topics/medical-imaging/ai-radiology-researchers-rsna-citations-downloads?utm_source=newsletter&utm_medium=ai_news

GE Healthcare launches new program to simplify AI development, implementation

Michael Walter | November 26, 2019 | Business Intelligence

https://www.aiin.healthcare/topics/business-intelligence/ge-healthcare-new-program-simplify-ai-development?utm_source=newsletter&utm_medium=ai_news

How teleradiologists are helping underserved regions all over the world

Michael Walter | Medical Imaging Review

Sponsored by vRad, a MEDNAX Company

https://www.radiologybusiness.com/sponsored/1065/topics/medical-imaging-review/qa-how-teleradiologists-are-helping-underserved?utm_source=newsletter&utm_medium=ai_news

AI in Healthcare 2020 Leadership Survey Report: 7 Key Findings

Mary C. Tierney, MS | AI in Healthcare 2020 Leadership Survey Report

Artificial and augmented intelligence are already helping healthcare improve clinically, operationally and financially—and there is extraordinary room for growth. Success starts with leadership, vision and investment and leaders tell us they have all of the above. Here are the top 7 survey findings.

01 C-level healthcare leaders are leading the charge to AI. AI has earned the attention of the C-suite, with 40% of survey respondents saying their strategy is coming from the top down. Chief information officers are most often managing AI across the healthcare enterprise (27%).

02 AI has moved into the mainstream. The future is now. It’s here. Health systems are hiring data scientists and spending on AI and infrastructure. Some 40% of respondents are using AI, with 50% using between one and 10 apps.

03 Health systems are committed to investing in AI. 93% of respondents agree AI is absolutely essential, very important or important to their strategy. There is great willingness to take advantage of intelligent technology and leverage machine intelligence to enhance human intelligence. Administration holds financial responsibility for AI at 43% of facilities, with IT paying the bill at 26% of sites.

04 Fortifying infrastructure is top of mind. 93% of respondents agree AI is absolutely essential, very important or important to their strategy. There is great willingness to take advantage of intelligent technology and leverage machine
intelligence to enhance human intelligence. Administration holds financial responsibility for AI at 43% of facilities, with IT paying the bill at 26% of sites.

05 Improving care is AI’s greatest benefit. Improving accuracy, efficiency and workflow are the top benefits leaders see coming from AI. AI helps to highlight key findings from the depths of the EMR, identify declines in patient conditions earlier and improve chronic disease management. Cancer, heart disease and stroke are the disease states survey respondents see AI holding the greatest promise—the 2nd, 1st and 5th leading killer of Americans.

06 Health systems are both buying and developing AI apps. Some 50% of respondents tell us they are both buying and developing AI apps. About 38% are exclusively opting to purchase commercially developed apps while 13% are developing everything in-house.

07 Radiology is blazing the AI trail. AI apps for imaging outnumber all other categories of FDA-approved apps to date. It’s no surprise then that respondents tell us that rad apps top the list of tools they’re using to enhance breast, chest and cardiovascular imaging.

SOURCE

https://www.aiin.healthcare/sponsored/9667/topics/ai-healthcare-2020-leadership-survey-report/ai-healthcare-2020-leadership-1

WATCH VIDEO

https://www.dropbox.com/s/xayeu7ss7f7cahp/AI%20Launch%20v2.mp4?dl=0

Like in the past, Dr. Eric Topol is a Tour de Force, again

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again 1st Edition

by Eric Topol (Author)

https://www.amazon.com/gp/product/1541644638/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=wwwsamharris03-20&creative=9325&linkCode=as2&creativeASIN=1541644638&linkId=e8e2d5410e9b5921f1e21883a9c84cff

Dr Mike Warner

5.0 out of 5 starsCrystal Ball for the Next Era of Healthcare

March 13, 2019

Format: HardcoverVerified Purchase

Dr. Topol’s new book, Deep Medicine – How Artificial Intelligence Can Make Healthcare Human Again, is an encyclopedia of the emerging Fourth Industrial Age; a crystal ball in what is about happen in the next era of healthcare. I’m impressed by the detailed references and touching personal and family stories.

Centers for Medicare & Medicaid Services (CMS) policy modifications in the past 10 months reveal sweeping changes that fortify Dr. Topol’s vision: May 2018 medical students can document for attending physicians in the health record (MLN MM10412), 2019 ancillary staff members and patients can document the History/medical interview into the health record, 2021 medical providers can document based only on Medical Decision Making or Time (Federal Register Nov, 23, 2018).

Part of making healthcare human is also making it fun. The joy of practicing medicine is about to return to the healthcare delivery as computers will be used to empower humanistic traits, not overburden medical professionals with clerical tasks. For patients, you will be heard, understood and personally treated. Deep Medicine is not a vision of what will happen in 50 years as much will start to reveal within the next 5!

Bravo Dr. Topol!
Michael Warner, DO, CPC, CPCO, CPMA, AAPC Fellow

https://www.amazon.com/gp/product/1541644638/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=wwwsamharris03-20&creative=9325&linkCode=as2&creativeASIN=1541644638&linkId=e8e2d5410e9b5921f1e21883a9c84cff#customerReviews

AUDIT PODCASTS

The perspective of what it truly means to be an AI company and AI platform.
How MaxQ AI is reinventing the diagnostic process with AI in time sensitive, life threatening environments.
How EnvoyAI is working towards a zero-click approach for physicians to feel confident in their findings.
Recognizing the right questions to ask when training algorithms for more accurate results.
The value of having a powerful world-class image processing algorithm running on an extensible interoperable platform.

Join Jeff, Gene, and Kevin next time as they continue the conversation on the future of artificial intelligence in healthcare.

https://www.terarecon.com/blog/beyond-the-screen-episode-6-next-generation-ai-companies-providing-physicians-a-starting-point-in-ai?utm_campaign=AuntMinnie%20June%202019&utm_medium=email&utm_source=hs_email

Academic Gallup Poll: The Artificial Intelligence Age, June 2019.

New Northeastern-Gallup poll: People in the US, UK, and Canada want to keep up in the artificial intelligence age. They say employers, educators, and governments are letting them down. – News @ Northeastern

https://news.northeastern.edu/2019/06/27/new-northeastern-gallup-poll-people-in-the-us-uk-and-canada-want-to-keep-up-in-the-artificial-intelligence-age-they-say-employers-educators-and-governments-are-letting-them-down/

Dense Map of Artificial Intelligence Start ups in Israel

Image Source: https://www.startuphub.ai/multinational-corporations-with-artificial-intelligence-research-and-development-centers-in-israel/

(See here for an interactive version of the infographic above).

https://www.forbes.com/sites/gilpress/2018/09/24/the-thriving-ai-landscape-in-israel-and-what-it-means-for-global-ai-competition/#577a107330c5

https://hackernoon.com/israels-artificial-intelligence-landscape-2018-83cdd4f04281

3.1 The Science

VIEW VIDEO

Max Tegmark lecture on Life 3.0 – Being Human in the age of Artificial Intelligence

https://www.youtube.com/watch?v=1MqukDzhlqA

3.1.1 World Medical Innovation Forum, Partners Innovations, ARTIFICIAL INTELLIGENCE | APRIL 8–10, 2019 | Westin, BOSTON

https://worldmedicalinnovation.org/agenda/

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/02/14/world-medical-innovation-forum-partners-innovations-artificial-intelligence-april-8-10-2019-westin-boston/

3.1.2 LIVE Day Three – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 10, 2019

Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/04/10/live-day-three-world-medical-innovation-forum-artificial-intelligence-boston-ma-usa-monday-april-10-2019/

3.1.3 LIVE Day Two – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 9, 2019

Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/04/09/live-day-two-world-medical-innovation-forum-artificial-intelligence-boston-ma-usa-monday-april-9-2019/

3.1.4 LIVE Day One – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 8, 2019

Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/04/08/live-day-one-world-medical-innovation-forum-artificial-intelligence-westin-copley-place-boston-ma-usa-monday-april-8-2019/

3.1.5 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place https://worldmedicalinnovation.org/

Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/01/18/2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachusetts-westin-copley-place/

3.1.6 Synopsis Days 1,2,3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place

Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/04/26/synopsis-days-123-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachusetts-westin-copley-place/

3.1.7 Interview with Systems Immunology Expert Prof. Shai Shen-Orr

Reporter: Aviva Lev-Ari, PhD, RN

https://tmrwedition.com/2018/07/19/interview-with-systems-immunology-expert-prof-shai-shen-orr/

3.1.8 Unique immune-focused AI model creates largest library of inter-cellular communications at CytoReason. Used to predict 335 novel cell-cytokine interactions, new clues for drug development.

Reporter: Aviva Lev-Ari, PhD, RN

CYTOREASON. CytoReason features in hashtag #DeepKnowledgeVentures‘s detailed Report on AI in hashtag #drugdevelopment report https://lnkd.in/dKV2BB6

https://www.eurekalert.org/pub_releases/2018-06/c-uia061818.php

3.2 Technologies and Methodologies

3.2.1 R&D for Artificial Intelligence Tools & Applications: Google’s Research Efforts in 2018

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/01/16/rd-for-artificial-intelligence-tools-applications-googles-research-efforts-in-2018/

3.2.2 Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

https://pharmaceuticalintelligence.com/2018/12/10/can-blockchain-technology-and-artificial-intelligence-cure-what-ails-biomedical-research-and-healthcare/

3.2.3 N3xt generation carbon nanotubes

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/12/14/n3xt-generation-carbon-nanotubes/

3.2.4 Mindful Discoveries

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/01/28/mindful-discoveries/

3.2.5 Novel Discoveries in Molecular Biology and Biomedical Science

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/05/30/novel-discoveries-in-molecular-biology-and-biomedical-science/

3.2.6 Imaging of Cancer Cells

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/04/20/imaging-of-cancer-cells/

3.2.7 Retrospect on HistoScanning: an AI routinely used in diagnostic imaging for over a decade

Author and Curator: Dror Nir, PhD

https://pharmaceuticalintelligence.com/2019/06/22/retrospect-on-histoscanning-an-ai-routinely-used-in-diagnostic-imaging-for-over-a-decade/

3.2.8 Prediction of Cardiovascular Risk by Machine Learning (ML) Algorithm: Best performing algorithm by predictive capacity had area under the ROC curve (AUC) scores: 1st, quadratic discriminant analysis; 2nd, NaiveBayes and 3rd, neural networks, far exceeding the conventional risk-scaling methods in Clinical Use

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/07/04/prediction-of-cardiovascular-risk-by-machine-learning-ml-algorithm-best-performing-algorithm-by-predictive-capacity-had-area-under-the-roc-curve-auc-scores-1st-quadratic-discriminant-analysis/

3.2.9 An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2019/07/24/an-intelligent-dna-nanorobot-to-fight-cancer-by-targeting-her2-expression/

3.3 Clinical Aspects

Is AI ready for Medical Applications? – The Debate in August 2019 in Nature

Eric Topol (@EricTopol)

8/18/19, 2:17 PM

Why I’ve been writing #AI for medicine is long on promise, short of proof

nature.com/articles/s4159… ⁦@NatureMedicine⁩

status update in this schematic, among many mismatches pic.twitter.com/mpifYFwlp8

The “inconvenient truth” about AI in healthcare

npj Digital Medicine volume 2, Article number: 77 (2019) | Download Citation

However, “the inconvenient truth” is that at present the algorithms that feature prominently in research literature are in fact not, for the most part, executable at the frontlines of clinical practice. This is for two reasons: first, these AI innovations by themselves do not re-engineer the incentives that support existing ways of working.² A complex web of ingrained political and economic factors as well as the proximal influence of medical practice norms and commercial interests determine the way healthcare is delivered. Simply adding AI applications to a fragmented system will not create sustainable change. Second, most healthcare organizations lack the data infrastructure required to collect the data needed to optimally train algorithms to (a) “fit” the local population and/or the local practice patterns, a requirement prior to deployment that is rarely highlighted by current AI publications, and (b) interrogate them for bias to guarantee that the algorithms perform consistently across patient cohorts, especially those who may not have been adequately represented in the training cohort.⁹ For example, an algorithm trained on mostly Caucasian patients is not expected to have the same accuracy when applied to minorities.¹⁰ In addition, such rigorous evaluation and re-calibration must continue after implementation to track and capture those patient demographics and practice patterns which inevitably change over time.¹¹ Some of these issues can be addressed through external validation, the importance of which is not unique to AI, and it is timely that existing standards for prediction model reporting are being updated specifically to incorporate standards applicable to this end.¹² In the United States, there are islands of aggregated healthcare data in the ICU,¹³ and in the Veterans Administration.¹⁴ These aggregated data sets have predictably catalyzed an acceleration in AI development; but without broader development of data infrastructure outside these islands it will not be possible to generalize these innovations.

https://www.nature.com/articles/s41746-019-0155-4

3.3.1 9 AI-based initiatives catalyzing immunotherapy in 2018

By Tanima Bose

https://www.prescouter.com/2018/07/9-ai-based-initiatives-catalyzing-immunotherapy-in-2018/

3.3.2 mRNA Data Survival Analysis

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/06/18/mrna-data-survival-analysis/

3.3.3 Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

Reporter: Stephen J. Williams

https://pharmaceuticalintelligence.com/2018/07/11/medcity-converge-2018-philadelphia-live-coverage-pharma_bi/

3.3.4 Live Coverage: MedCity Converge 2018 Philadelphia: AI in Cancer and Keynote Address

Reporter: Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2018/07/11/live-coverage-medcity-converge-2018-philadelphia-ai-in-cancer-and-keynote-address/

3.3.5 VIDEOS: Artificial Intelligence Applications for Cardiology

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/03/11/videos-artificial-intelligence-applications-for-cardiology/

3.3.6 Artificial Intelligence in Health Care and in Medicine: Diagnosis & Therapeutics

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/01/21/artificial-intelligence-in-health-care-and-in-medicine-diagnosis-therapeutics/

3.3.7 Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2019/03/18/digital-therapeutics-a-threat-or-opportunity-to-pharmaceuticals/

3.3.8 The 3^rd STATONC Annual Symposium, April 25-27, 2019, Hilton Hartford, CT, 315 Trumbull St., Hartford, CT 06103

Reporter: Stephen J. Williams, Ph.D.

https://pharmaceuticalintelligence.com/2019/02/26/the-3rd-stat4onc-annual-symposium-april-25-27-2019-hilton-hartford-connecticut/

3.3.9 2019 Biotechnology Sector and Artificial Intelligence in Healthcare

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/05/10/2019-biotechnology-sector-and-artificial-intelligence-in-healthcare/

3.3.10 Artificial intelligence can be a useful tool to predict Alzheimer

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/01/26/artificial-intelligence-can-be-a-useful-tool-to-predict-alzheimer/

3.3.11 Unlocking the Microbiome

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/02/07/unlocking-the-microbiome/

3.3.12 Biomarker Development

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/11/16/biomarker-development/

3.3.13 AI System Used to Detect Lung Cancer

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

3.3.14 AI App for People with Digestive Disorders

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/06/24/ai-app-for-people-with-digestive-disorders/

3.3.15 Sepsis Detection using an Algorithm More Efficient than Standard Methods

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/06/25/sepsis-detection-using-an-algorithm-more-efficient-than-standard-methods/

3.3.16 How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancer?

Author: Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2013/03/20/how-might-sleep-apnea-lead-to-serious-health-concerns-like-cardiac-and-cancers/

3.3.17 An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2019/07/24/an-intelligent-dna-nanorobot-to-fight-cancer-by-targeting-her2-expression/

3.3.18 Artificial Intelligence and Cardiovascular Disease

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2019/07/26/artificial-intelligence-and-cardiovascular-disease/

3.3.19 Using A.I. to Detect Lung Cancer gets an A!

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/08/04/using-a-i-to-detect-lung-cancer-gets-an-a/

3.3.20 Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Reporter: Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2019/08/14/complex-rearrangements-and-oncogene-amplification-revealed-by-long-read-dna-and-rna-sequencing-of-a-breast-cancer-cell-line/

3.3.21 Multiple Barriers Identified Which May Hamper Use of Artificial Intelligence in the Clinical Setting

Reporter: Stephen J. Williams, PhD.

https://pharmaceuticalintelligence.com/2019/07/21/multiple-barriers-identified-which-may-hamper-use-of-artificial-intelligence-in-the-clinical-setting/

3.3.22 Deep Learning–Assisted Diagnosis of Cerebral Aneurysms

Author and Curator: Dror Nir, PhD

https://pharmaceuticalintelligence.com/2019/06/09/deep-learning-assisted-diagnosis-of-cerebral-aneurysms/

3.3.23 Artificial Intelligence Innovations in Cardiac Imaging

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/12/17/artificial-intelligence-innovations-in-cardiac-imaging/

3.4 Business and Legal

Image Source: https://www.linkedin.com/pulse/resources-artificial-intelligence-health-care-note-lev-ari-phd-rn/

3.4.1 McKinsey Top Ten Articles on Artificial Intelligence: 2018’s most popular articles – An executive’s guide to AI

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/01/21/mckinsey-top-ten-articles-on-artificial-intelligence-2018s-most-popular-articles-an-executives-guide-to-ai/

3.4.2 HOTTEST Artificial Intelligence Hub: Israel’s High Tech Industry – Why?

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/09/30/hottest-artificial-intelligence-hub-israels-high-tech-industry-why/

3.4.3 The Regulatory challenge in adopting AI

Author and Curator: Dror Nir, PhD

https://pharmaceuticalintelligence.com/2019/04/07/the-regulatory-challenge-in-adopting-ai/

3.4.4 HealthCare focused AI Startups from the 100 Companies Leading the Way in A.I. Globally

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/01/18/healthcare-focused-ai-startups-from-the-100-companies-leading-the-way-in-a-i-globally/

3.4.5 IBM’s Watson Health division – How will the Future look like?

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/04/24/ibms-watson-health-division-how-will-the-future-look-like/

3.4.6 HUBweek 2018, October 8-14, 2018, Greater Boston – “We The Future” – coming together, of breaking down barriers, of convening across disciplinary lines to shape our future

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/10/08/hubweek-2018-october-8-14-2018-greater-boston-we-the-future-coming-together-of-breaking-down-barriers-of-convening-across-disciplinary-lines-to-shape-our-future/

3.4.7 Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?

Reporter: Stephen J. Williams, Ph.D.

https://pharmaceuticalintelligence.com/2019/01/03/role-of-informatics-in-precision-medicine-can-it-drive-the-next-cost-efficiencies-in-oncology-care/

3.4.8 Healthcare conglomeration to access Big Data and lower costs

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/01/13/healthcare-conglomeration-to-access-big-data-and-lower-costs/

3.4.9 Linguamatics announces the official launch of its AI self-service text-mining solution for researchers.

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/05/10/linguamatics-announces-the-official-launch-of-its-ai-self-service-text-mining-solution-for-researchers/

3.4.10 Future of Big Data for Societal Transformation

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/12/14/future-of-big-data-for-societal-transformation/

3.4.11 Deloitte Analysis 2019 Global Life Sciences Outlook

https://www2.deloitte.com/global/en/pages/life-sciences-and-healthcare/articles/global-life-sciences-sector-outlook.html

https://www.cioapplications.com/news/making-a-breakthrough-in-drug-discovery-with-ai-nid-3114.html

https://healthcare.cioapplications.com/cioviewpoint/leveraging-technologies-to-better-position-the-business-nid-1060.html

3.4.12 OpenAI: $1 Billion to Create Artificial Intelligence Without Profit Motive by Who is Who in the Silicon Valley

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2015/12/26/openai-1-billion-to-create-artificial-intelligence-without-profit-motive-by-who-is-who-in-the-silicon-valley/

3.4.13 The Health Care Benefits of Combining Wearables and AI

Reporter: Gail S. Thornton, M.A.

https://pharmaceuticalintelligence.com/2019/07/02/the-health-care-benefits-of-combining-wearables-and-ai/

3.4.14 These twelve artificial intelligence innovations are expected to start impacting clinical care by the end of the decade.

Reporter: Gail S. Thornton, M.A.

https://pharmaceuticalintelligence.com/2019/07/02/top-12-artificial-intelligence-innovations-disrupting-healthcare-by-2020/

3.4.15 Forbes Opinion: 13 Industries Soon To Be Revolutionized By Artificial Intelligence

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/07/31/forbes-opinion-13-industries-soon-to-be-revolutionized-by-artificial-intelligence/

3.4.16 AI Acquisitions by Big Tech Firms Are Happening at a Blistering Pace: 2019 Recent Data by CBI Insights

Reporter: Stephen J. Williams, Ph.D.

https://pharmaceuticalintelligence.com/2019/12/11/ai-acquisitions-by-big-tech-firms-are-happening-at-a-blistering-pace-2019-recent-data-by-cbiinsights/

3.5 Machine Learning (ML) Algorithms harnessed for Medical Diagnosis: Pattern Recognition & Prediction of Disease Onset

Introduction by Dr. Dror Nir

Icahn School of Medicine at Mount Sinai to Establish World Class Center for Artificial Intelligence – Hamilton and Amabel James Center for Artificial Intelligence and Human Health

First center in New York to seamlessly integrate artificial intelligence, data science and genomic screening to advance clinical practice and patient outcomes.

Integrative Omics and Multi-Scale Disease Modeling— Artificial intelligence and machine learning approaches developed at the Icahn Institute have been extensively used for identification of novel pathways, drug targets, and therapies for complex human diseases such as cancer, Alzheimer’s, schizophrenia, obesity, diabetes, inflammatory bowel disease, and cardiovascular disease. Researchers will combine insights in genomics—including state-of-the-art single-cell genomic data—with ‘omics,’ such as epigenomics, pharmacogenomics, and exposomics, and integrate this information with patient health records and data originating from wearable devices in order to model the molecular, cellular, and circuit networks that facilitate disease progression. “Novel data-driven predictions will be tightly integrated with high-throughput experiments to validate the therapeutic potential of each prediction,” said Adam Margolin, PhD, Professor and Chair of the Department of Genetics and Genomic Sciences and Senior Associate Dean of Precision Medicine at Mount Sinai. “Clinical experts in key disease areas will work side-by-side with data scientists to translate the most promising therapies to benefit patients. We have the potential to transform the way care givers deliver cost-effective, high quality health care to their patients, far beyond providing simple diagnoses. Mount Sinai wants to be on the frontlines of discovery.”

Precision Imaging—Researchers will use artificial intelligence to enhance the diagnostic power of imaging technologies—X-ray, MRI, CT, and PET—and molecular imaging, and accelerate the development of therapies. “We see a huge potential in using algorithms to automate the image interpretation and to acquire images much more quickly at high resolution – so that we can better detect disease and make it less burdensome for the patient,” said Zahi Fayad, PhD, Director of the Translational and Molecular Imaging Institute, and Vice Chair for Research for the Department of Radiology, at Mount Sinai. Dr. Fayad plans to broaden the scope of the Translational and Molecular Imaging Institute by recruiting more engineers and scientists who will create new methods to aid in the diagnosis and early detection of disease, treatment protocol development, drug development, and personalized medicine. Dr. Fayad added, “In addition to AI, we envision advance capabilities in two important areas: computer vision and augmented reality, and next generation medical technology enabling development of new medical devices, sensors and robotics.”

https://www.mountsinai.org/about/newsroom/2019/icahn-school-of-medicine-at-mount-sinai-to-establish-world-class-center-for-artificial-intelligence-hamilton-and-amabel-james-center-for-artificial-intelligence-and-human-health

A comprehensive overview of ML algorithms applied in health care is presented in the following article:

Survey of Machine Learning Algorithms for Disease Diagnostic

https://www.scirp.org/journal/PaperInformation.aspx?PaperID=73781

3.5.1 Cases in Pathology

3.5.1.1 Deep Learning extracts Histopathological Patterns and accurately discriminates 28 Cancer and 14 Normal Tissue Types: Pan-cancer Computational Histopathology Analysis

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/10/28/deep-learning-extracts-histopathological-patterns-and-accurately-discriminates-28-cancer-and-14-normal-tissue-types-pan-cancer-computational-histopathology-analysis/

3.5.2 Cases in Radiology

3.5.2.1 Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/10/29/cardiac-mri-imaging-breakthrough-the-first-ai-assisted-cardiac-mri-scan-solution-heartvista-receives-fda-510k-clearance-for-one-click-cardiac-mri-package/

3.5.2.2 Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI

Reporter: Dror Nir, PhD

https://pharmaceuticalintelligence.com/2019/08/01/disentangling-molecular-alterations-from-water-content-changes-in-the-aging-human-brain-using-quantitative-mri/

3.5.2.3 Showcase: How Deep Learning could help radiologists spend their time more efficiently

Reporter and Curator: Dror Nir, PhD

https://pharmaceuticalintelligence.com/2019/08/22/showcase-how-deep-learning-could-help-radiologists-spend-their-time-more-efficiently/

3.5.2.4 CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to anyone on Earth

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/07/28/cancerbase-org-the-global-hub-for-diagnoses-genomes-pathology-images-a-real-time-diagnosis-and-therapy-mapping-service-for-cancer-patients-anonymized-medical-records-accessible-to/

3.5.2.5 Applying AI to Improve Interpretation of Medical Imaging

Author and Curator: Dror Nir, PhD

https://pharmaceuticalintelligence.com/2019/05/28/applying-ai-to-improve-interpretation-of-medical-imaging/

3.5.2.6 Imaging: seeing or imagining? (Part 2)

Author and Curator: Dror Nir, PhD

https://pharmaceuticalintelligence.com/2019/04/07/imaging-seeing-or-imagining-part-2-2/

3.5.3 Cases in Prediction Cancer Onset

3.5.3.1 A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

Published Online: May 7 2019 Radiology Vol. 292, No. 1 https://doi.org/10.1148/radiol.2019182716

3.5.3.2 Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction

Karin Dembrower , Yue Liu, Hossein Azizpour, Martin Eklund, Kevin Smith, Peter Lindholm, Fredrik Strand

Author Affiliations

Published Online: Dec 17 2019 https://doi.org/10.1148/radiol.2019190872

See editorial by Manisha Bahl

Results

A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P < .001), the dense area (mean, 38.2 cm² vs 34.2 cm²; P < .001), and the percentage density (mean, 25.6% vs 24.0%; P < .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P < .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P < .001); this difference was most pronounced for more aggressive cancers.

Conclusion

Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers.

Related articles

Harnessing the Power of Deep Learning to Assess Breast Cancer Risk

Radiology2019

Volume: 0Issue: 0

Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives

Radiology2019

Volume: 293Issue: 2pp. 246-259

Digital 2D versus Tomosynthesis Screening Mammography among Women Aged 65 and Older in the United States

Radiology2019

Volume: 291Issue: 3pp. 582-590

Summary of ML in Medicine by Dr. Dror Nir

See Introduction to 3.5, above

Part 3: Summary – AI in Medicine – Voice of Aviva Lev-Ari & Professor Williams

AI applications in healthcare

The potential of AI to improve the healthcare delivery system is limitless. It offers a unique opportunity to make sense out of clinical data to enable fully integrated healthcare that is more predictive and precise. Getting all aspects of AI-enabled solutions right requires extensive collaboration between clinicians, data scientists, interaction designers, and other experts. Here are four applications of artificial intelligence to transform healthcare delivery:

1. Improve operational efficiency and performance

On a departmental and enterprise level, the ability of AI to sift through large amounts of data can help hospital administrators to optimize performance, drive productivity, and improve the use of existing resources, generating time and cost savings. For example, in a radiology department, AI could make a difference in the management of referrals, patient scheduling, and exam preparations. Improvements here can help to enhance patient experience and will allow a more effective and efficient use of the facilities at examination sites.

2. Aiding clinical decision support

AI-enabled solutions can help to combine large amounts of clinical data to generate a more holistic view of patients. This supports healthcare providers in their decision making, leading to better patient outcomes and improved population health. “The need for insights and for those insights to lead to clinical operations support is tremendous,” says Dr. Smythe. “Whether that is the accuracy of interventions or the effective use of manpower – these are things that physicians struggle with. That is the imperative.”

3. Enabling population health management

Combining clinical decision support systems with patient self-management, population health management can also benefit from AI. Using predictive analytics with patient populations, healthcare providers will be able to take preventative action, reduce health risk, and save unnecessary costs.

As the population ages, so does a desire to age in place when possible, and to maximize not only disease management, but quality of life as we do so. The possibility of aggregating, analyzing and activating health data from millions of consumers will enable hospitals to see how socio-economic, behavioral, genetic and clinical factors correlate and can offer more targeted, preventative healthcare outside the four walls of the hospital.

4. Empowering consumers, improving patient care

As recently as 2015 patients reported physically carrying x-rays, test results, and other critical health data from one healthcare provider’s office to another³. The burden of multiple referrals, explaining symptoms to new physicians and finding out that their medical history has gaps in it were all too real. Patients now are demanding more personalized, sophisticated and convenient healthcare services.

The great motivation behind AI in healthcare is that increasingly, as patients become more engaged with their own healthcare and better understand their own needs, healthcare will have to take steps towards them and meet them where they are, providing them with health services when they need them, not just when they are ill.

SOURCE

https://www.usa.philips.com/healthcare/nobounds/four-applications-of-ai-in-healthcare?origin=1_us_en_auntminnie_aicommunity

Our Summary for AI in Medicine presents to the eReader the results of the 2020 Survey on that topic, all the live links will take the eReader to the report itself. We provided the reference, below

AI in Healthcare 2020 Leadership Survey Report: About the Survey

Mary C. Tierney, MS | AI in Healthcare 2020 Leadership Survey Report

The AI in Healthcare team embarked on this survey to gain a deeper understanding of the current state of artificial and augmented intelligence in use and being planned across healthcare in the next few years. We polled readers of AI in Healthcare, AIin.Healthcare and sister brand HealthExec.com over 2 months. All data is presented in this report in aggregate, with individual responses remaining anonymous.

The content in this report reflects the input of 1,238 physicians, executives, IT and administrative leaders in healthcare, medical devices and IT and software development from across the globe, with 75 percent based in the United States. The report focuses on the responses of providers and professionals at the helm of healthcare systems, integrated delivery networks, academic medical centers, hospitals, imaging centers and physician groups across the U.S. For a deeper dive into survey demographics, click here.

Some respondents chose to share more specific demographics that help us better get to know our survey base. Those 165 healthcare leaders work for 38 unique health systems, hospitals, physician groups and imaging or surgery centers, across 39 states and the District of Columbia. They are large, small and mid-sized, for profit, not for profit, academic and government owned. Respondents, too, herald from all levels of leadership. Here are some of the interesting titles who chimed in—and we are thankful they did: CEO, CFO, CMO, CIO, chief innovation officer, chief data officer, chief administrative officer, medical director of quality, senior VP of quality and innovation officer, system director of transformation, VP of service line development, and plenty of physicians, directors of ICU, imaging, cath lab and surgery, nurses and technologists.

In this report we unpack current trends in AI and machine learning, drill into data from various perspectives such as the C-suite and the physician leader, and learn how healthcare systems are using and planning to use AI. Turn the page and see where we are and where we’re going.

Author: Mary C. Tierney, MS, Chief Content Officer, AI in Healthcare magazine and AIin.Healthcare

AI in Healthcare 2020 Leadership Survey Report

SOURCE

https://www.aiin.healthcare/sponsored/9667/topics/ai-healthcare-2020-leadership-survey-report/ai-healthcare-2020-leadership-3

AI Acquisitions by Big Tech Firms Are Happening at a Blistering Pace: 2019 Recent Data by CBIInsights

Posted in An executive's guide to AI, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data, Intelligent Information Systems, Transformative Technologies in Healthcare, tagged AI, Amazon, Apple, Artificial intelligence, Big data, big tech, early ventures, Facebook, Google, Machine Learning, Mergers and acquisitions, microsoft, startup on December 11, 2019| Leave a Comment »

AI Acquisitions by Big Tech Firms Are Happening at a Blistering Pace: 2019 Recent Data by CBI Insights

Reporter: Stephen J. Williams, Ph.D.

3.4.16 AI Acquisitions by Big Tech Firms Are Happening at a Blistering Pace: 2019 Recent Data by CBI Insights, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 3: AI in Medicine

Recent report from CBI Insights shows the rapid pace at which the biggest tech firms (Google, Apple, Microsoft, Facebook, and Amazon) are acquiring artificial intelligence (AI) startups, potentially confounding the AI talent shortage that exists.

The link to the report and free download is given here at https://www.cbinsights.com/research/top-acquirers-ai-startups-ma-timeline/

Part of the report:

TECH GIANTS LEAD IN AI ACQUISITIONS

The usual suspects are leading the race for AI: tech giants like Facebook, Amazon, Microsoft, Google, & Apple (FAMGA) have all been aggressively acquiring AI startups in the last decade.

Among the FAMGA companies, Apple leads the way, making 20 total AI acquisitions since 2010. It is followed by Google (the frontrunner from 2012 to 2016) with 14 acquisitions and Microsoft with 10.

Apple’s AI acquisition spree, which has helped it overtake Google in recent years, was essential to the development of new iPhone features. For example, FaceID, the technology that allows users to unlock their iPhone X just by looking at it, stems from Apple’s M&A moves in chips and computer vision, including the acquisition of AI company RealFace.

In fact, many of FAMGA’s prominent products and services came out of acquisitions of AI companies — such as Apple’s Siri, or Google’s contributions to healthcare through DeepMind.

That said, tech giants are far from the only companies snatching up AI startups.

Since 2010, there have been 635 AI acquisitions, as companies aim to build out their AI capabilities and capture sought-after talent (as of 8/31/2019).

The pace of these acquisitions has also been increasing. AI acquisitions saw a more than 6x uptick from 2013 to 2018, including last year’s record of 166 AI acquisitions — up 38% year-over-year.

In 2019, there have already been 140+ acquisitions (as of August), putting the year on track to beat the 2018 record at the current run rate.

Part of this increase in the pace of AI acquisitions can be attributed to a growing diversity in acquirers. Where once AI was the exclusive territory of major tech companies, today, smaller AI startups are becoming acquisition targets for traditional insurance, retail, and healthcare incumbents.

For example, in February 2018, Roche Holding acquired New York-based cancer startup Flatiron Health for $1.9B — one of the largest M&A deals in artificial intelligence. This year, Nike acquired AI-powered inventory management startup Celect, Uber acquired computer vision company Mighty AI, and McDonald’s acquired personalization platform Dynamic Yield.

Despite the increased number of acquirers, however, tech giants are still leading the charge. Acquisitive tech giants have emerged as powerful global corporations with a competitive advantage in artificial intelligence, and startups have played a pivotal role in helping these companies scale their AI initiatives.

Apple, Google, Microsoft, Facebook, Intel, and Amazon are the most active acquirers of AI startups, each acquiring 7+ companies.

To read more on recent Acquisitions in the AI space please see the following articles on this Open Access Online Journal

Diversification and Acquisitions, 2001 – 2015: Trail known as “Google Acquisitions” – Understanding Alphabet’s Acquisitions: A Sector-By-Sector Analysis

Clarivate Analytics expanded IP data leadership by new acquisition of the leading provider of intellectual property case law and analytics Darts-ip

2019 Biotechnology Sector and Artificial Intelligence in Healthcare

Forbes Opinion: 13 Industries Soon To Be Revolutionized By Artificial Intelligence

Artificial Intelligence and Cardiovascular Disease

Multiple Barriers Identified Which May Hamper Use of Artificial Intelligence in the Clinical Setting

Top 12 Artificial Intelligence Innovations Disrupting Healthcare by 2020

The launch of SCAI – Interview with Gérard Biau, director of the Sorbonne Center for Artificial Intelligence (SCAI).

http://ipfconline.fr/blog/2018/03/29/50-contemporary-artificial-intelligence-leading-experts-and-researchers/

50 Contemporary Artificial Intelligence Leading Experts and Researchers

Posted in An executive's guide to AI, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General on May 26, 2019| Leave a Comment »

50 CONTEMPORARY ARTIFICIAL INTELLIGENCE LEADING EXPERTS AND RESEARCHERS

Reporter: Aviva Lev-Ari, PhD, RN

SOURCE

Pierre Pinna

PIERRE PINNA

http://www.ipfconline.fr

IPFConline Digital Innovations Founder / Artificial Intelligence Engineer (Natural Language Processing Specialist) >>> Providing expert conferences (English & French) in: AI – Fintech – Insurtech – GrowthHacking – DigitalMarketing – SEO – BigData – Analytics – eLearning – Ethics/Privacy- IoT & DataScience

March 29, 2018

For those who like, love or are just interested or want to discover what is Artificial Intelligence, I have built this fine List of 50 Top Contemporary Artificial Intelligence Experts and Researchers. No ranking there, of course!

I’ve done too a “classification” among the AI Topics, but evidently all these leading figures are all, globally, specialists in Machine Learning.

Finally, this list is of course non exhaustive (many others could be there 😉)

MACHINE LEARNING

DEEP LEARNING

NEURAL NETWORKS

RECURRENT NEURAL NETWORKS (RNN)

Jurgen Schmidhuber

REINFORCEMENT LEARNING

David Silver

Marcus Hutter

Richard Sutton

GENERATIVE ADVERSARIAL NETWORKS (GAN)

COMPUTER VISION

NATURAL LANGUAGE PROCESSING (NLP)

Dag Kittlaus

HEALTH TECH – BIOINFORMATICS

ROBOTICS

DATA SCIENCE – BIG DATA – ALGORITHMS

AI GAME DEVELOPMENT

AI ETHICS

SINGULARITY

Raymond “Ray” Kurzweil

MACHINE LEARNING FOR ARTISTS

Gene Kogan

Posted in An executive's guide to AI, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial intelligence applications for cardiology, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, CE Mark & Global Regulatory Affairs: process management and strategic planning - GCP, FDA, FDA Regulatory Affairs, FDA, CE Mark & Global Regulatory Affairs: process management and strategic planning - GCP, GLP, ISO 14155, ISO 10993 for Product Registration: FDA & CE Mark for Development of Medical Devices and Diagnostics, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Uncategorized on April 7, 2019| Leave a Comment »

The Regulatory challenge in adopting AI

The Regulatory challenge in adopting AI

Author and Curator: Dror Nir, PhD

3.4.3 The Regulatory challenge in adopting AI, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

In the last couple of years we are witnessing a surge of AI applications in healthcare. It is clear now, that AI and its wide range of health-applications are about to revolutionize diseases’ pathways and the way the variety of stakeholders in this market interact.

Not surprisingly, the developing surge has waken the regulatory watchdogs who are now debating ways to manage the introduction of such applications to healthcare. Attributing measures to known regulatory checkboxes like safety, and efficacy is proving to be a complex exercise. How to align claims made by manufacturers, use cases, users’ expectations and public expectations is unclear. A recent demonstration of that is the so called “failure” of AI in social-network applications like FaceBook and Twitter in handling harmful materials.

‘Advancing AI in the NHS’ – is a report covering the challenges and opportunities of AI in the NHS. It is a modest contribution to the debate in such a timely and fast-moving field! I bring here the report’s preface and executive summary hoping that whoever is interested in reading the whole 50 pages of it will follow this link: f53ce9_e4e9c4de7f3c446fb1a089615492ba8c

Acknowledgements

We and Polygeia as a whole are grateful to Dr Dror Nir, Director, RadBee, whose insights

were valuable throughout the research, conceptualisation, and writing phases of this work; and to Dr Giorgio Quer, Senior Research Scientist, Scripps Research Institute; Dr Matt Willis, Oxford Internet Institute, University of Oxford; Professor Eric T. Meyer, Oxford Internet Institute, University of Oxford; Alexander Hitchcock, Senior Researcher, Reform; Windi Hari, Vice President Clinical, Quality & Regulatory, HeartFlow; Jon Holmes, co-founder and Chief Technology Officer, Vivosight; and Claudia Hartman, School of Anthropology & Museum Ethnography, University of Oxford for their advice and support.

Author affiliations

Lev Tankelevitch, University of Oxford

Alice Ahn, University of Oxford

Rachel Paterson, University of Oxford

Matthew Reid, University of Oxford

Emily Hilbourne, University of Oxford

Bryan Adriaanse, University of Oxford

Giorgio Quer, Scripps Research Institute

Dror Nir, RadBee

Parth Patel, University of Cambridge

All affiliations are at the time of writing.

Polygeia

Polygeia is an independent, non-party, and non-profit think-tank focusing on health and its intersection with technology, politics, and economics. Our aim is to produce high-quality research on global health issues and policies. With branches in Oxford, Cambridge, London and New York, our work has led to policy reports, peer-reviewed publications, and presentations at the House of Commons and the European Parliament. http://www.polygeia.com @Polygeia © Polygeia 2018. All rights reserved.

Foreword

Almost every day, as MP for Cambridge, I am told of new innovations and developments that show that we are on the cusp of a technological revolution across the sectors. This technology is capable of revolutionising the way we work; incredible innovations which could increase our accuracy, productivity and efficiency and improve our capacity for creativity and innovation.

But huge change, particularly through adoption of new technology, can be difficult to communicate to the public, and if we do not make sure that we explain carefully the real benefits of such technologies we easily risk a backlash. Despite good intentions, the care.data programme failed to win public trust, with widespread worries that the appropriate safeguards weren’t in place, and a failure to properly explain potential benefits to patients. It is vital that the checks and balances we put in place are robust enough to sooth public anxiety, and prevent problems which could lead to steps back, rather than forwards.

Previous attempts to introduce digital innovation into the NHS also teach us that cross-disciplinary and cross-sector collaboration is essential. Realising this technological revolution in healthcare will require industry, academia and the NHS to work together and share their expertise to ensure that technical innovations are developed and adopted in ways that prioritise patient health, rather than innovation for its own sake. Alongside this, we must make sure that the NHS workforce whose practice will be altered by AI are on side. Consultation and education are key, and this report details well the skills that will be vital to NHS adoption of AI. Technology is only as good as those who use it, and for this, we must listen to the medical and healthcare professionals who will rightly know best the concerns both of patients and their colleagues. The new Centre for Data Ethics and Innovation, the ICO and the National Data Guardian will be key in working alongside the NHS to create both a regulatory framework and the communications which win society’s trust. With this, and with real leadership from the sector and from politicians, focused on the rights and concerns of individuals, AI can be advanced in the NHS to help keep us all healthy.

Daniel Zeichner

MP for Cambridge

Chair, All-Party Parliamentary Group on Data Analytics

Executive summary

Artificial intelligence (AI) has the potential to transform how the NHS delivers care. From enabling patients to self-care and manage long-term conditions, to advancing triage, diagnostics, treatment, research, and resource management, AI can improve patient outcomes and increase efficiency. Achieving this potential, however, requires addressing a number of ethical, social, legal, and technical challenges. This report describes these challenges within the context of healthcare and offers directions forward.

Data governance

AI-assisted healthcare will demand better collection and sharing of health data between NHS, industry and academic stakeholders. This requires a data governance system that ensures ethical management of health data and enables its use for the improvement of healthcare delivery. Data sharing must be supported by patients. The recently launched NHS data opt-out programme is an important starting point, and will require monitoring to ensure that it has the transparency and clarity to avoid exploiting the public’s lack of awareness and understanding. Data sharing must also be streamlined and mutually beneficial. Current NHS data sharing practices are disjointed and difficult to negotiate from both industry and NHS perspectives. This issue is complicated by the increasing integration of ’traditional’ health data with that from commercial apps and wearables. Finding approaches to valuate data, and considering how patients, the NHS and its partners can benefit from data sharing is key to developing a data sharing framework. Finally, data sharing should be underpinned by digital infrastructure that enables cybersecurity and accountability.

Digital infrastructure

Developing and deploying AI-assisted healthcare requires high quantity and quality digital data. This demands effective digitisation of the NHS, especially within secondary care, involving not only the transformation of paper-based records into digital data, but also improvement of quality assurance practices and increased data linkage. Beyond data digitisation, broader IT infrastructure also needs upgrading, including the use of innovations such as wearable technology and interoperability between NHS sectors and institutions. This would not only increase data availability for AI development, but also provide patients with seamless healthcare delivery, putting the NHS at the vanguard of healthcare innovation.

Standards

The recent advances in AI and the surrounding hype has meant that the development of AI-assisted healthcare remains haphazard across the industry, with quality being difﬁcult to determine or varying widely. Without adequate product validation, including in

real-world settings, there is a risk of unexpected or unintended performance, such as sociodemographic biases or errors arising from inappropriate human-AI interaction. There is a need to develop standardised ways to probe training data, to agree upon clinically-relevant performance benchmarks, and to design approaches to enable and evaluate algorithm interpretability for productive human-AI interaction. In all of these areas, standardised does not necessarily mean one-size-ﬁts-all. These issues require addressing the speciﬁcs of AI within a healthcare context, with consideration of users’ expertise, their environment, and products’ intended use. This calls for a fundamentally interdisciplinary approach, including experts in AI, medicine, ethics, cognitive science, usability design, and ethnography.

Regulations

Despite the recognition of AI-assisted healthcare products as medical devices, current regulatory efforts by the UK Medicines and Healthcare Products Regulatory Agency and the European Commission have yet to be accompanied by detailed guidelines which address questions concerning AI product classiﬁcation, validation, and monitoring. This is compounded by the uncertainty surrounding Brexit and the UK’s future relationship with the European Medicines Agency. The absence of regulatory clarity risks compromising patient safety and stalling the development of AI-assisted healthcare. Close working partnerships involving regulators, industry members, healthcare institutions, and independent AI-related bodies (for example, as part of regulatory sandboxes) will be needed to enable innovation while ensuring patient safety.

The workforce

AI will be a tool for the healthcare workforce. Harnessing its utility to improve care requires an expanded workforce with the digital skills necessary for both developing AI capability and for working productively with the technology as it becomes commonplace.

Developing capability for AI will involve ﬁnding ways to increase the number of clinician-informaticians who can lead the development, procurement and adoption of AI technology while ensuring that innovation remains tied to the human aspect of healthcare delivery. More broadly, healthcare professionals will need to complement their socio-emotional and cognitive skills with training to appropriately interpret information provided by AI products and communicate it effectively to co-workers and patients.

Although much effort has gone into predicting how many jobs will be affected by AI-driven automation, understanding the impact on the healthcare workforce will require examining how jobs will change, not simply how many will change.

Legal liability

AI-assisted healthcare has implications for the legal liability framework: who should be held responsible in the case of a medical error involving AI? Addressing the question of liability will involve understanding how healthcare professionals’ duty of care will be impacted by use of the technology. This is tied to the lack of training standards for healthcare professionals to safely and effectively work with AI, and to the challenges of algorithm interpretability, with ”black-box” systems forcing healthcare professionals to blindly trust or distrust their output. More broadly, it will be important to examine the legal liability of healthcare professionals, NHS trusts and industry partners, raising questions

Recommendations

The NHS, the Centre for Data Ethics and Innovation, and industry and academic partners should conduct a review to understand the obstacles that the NHS and external organisations face around data sharing. They should also develop health data valuation protocols which consider the perspectives of patients, the NHS, commercial organisations, and academia. This work should inform the development of a data sharing framework.
The National Data Guardian and the Department of Health should monitor the NHS data opt-out programme and its approach to transparency and communication, evaluating how the public understands commercial and non-commercial data use and the handling of data at different levels of anonymisation.
The NHS, patient advocacy groups, and commercial organisations should expand public engagement strategies around data governance, including discussions about the value of health data for improving healthcare; public and private sector interactions in the development of AI-assisted healthcare; and the NHS’s strategies around data anonymisation, accountability, and commercial partnerships. Findings from this work should inform the development of a data sharing framework.
The NHS Digital Security Operations Centre should ensure that all NHS organisations comply with cybersecurity standards, including having up-to-date technology.
NHS Digital, the Centre for Data Ethics and Innovation, and the Alan Turing Institute should develop technological approaches to data privacy, auditing, and accountability that could be implemented in the NHS. This should include learning from Global Digital Exemplar trusts in the UK and from international examples such as Estonia.
The NHS should continue to increase the quantity, quality, and diversity of digital health data across trusts. It should consider targeted projects, in partnership with professional medical bodies, that quality-assure and curate datasets for more deployment-ready AI technology. It should also continue to develop its broader IT infrastructure, focusing on interoperability between sectors, institutions, and technologies, and including the end users as central stakeholders.
The Alan Turing Institute, the Ada Lovelace Institute, and academic and industry partners in medicine and AI should develop ethical frameworks and technological approaches for the validation of training data in the healthcare sector, including methods to minimise performance biases and validate continuously-learning algorithms.
The Alan Turing Institute, the Ada Lovelace Institute, and academic and industry partners in medicine and AI should develop standardised approaches for evaluating product performance in the healthcare sector, with consideration for existing human performance standards and products’ intended use.
The Alan Turing Institute, the Ada Lovelace Institute, and academic and industry partners in medicine and AI should develop methods of enabling and evaluating algorithm interpretability in the healthcare sector. This work should involve experts in AI, medicine, ethics, usability design, cognitive science, and ethnography, among others.
Developers of AI products and NHS Commissioners should ensure that usability design remains a top priority in their respective development and procurement of AI-assisted healthcare products.
The Medicines and Healthcare Products Regulatory Agency should establish a digital health unit with expertise in AI and digital products that will work together with manufacturers, healthcare bodies, notiﬁed bodies, AI-related organisations, and international forums to advance clear regulatory approaches and guidelines around AI product classiﬁcation, validation, and monitoring. This should address issues including training data and biases, performance evaluation, algorithm interpretability, and usability.
The Medicines and Healthcare Products Regulatory Agency, the Centre for Data Ethics and Innovation, and industry partners should evaluate regulatory approaches, such as regulatory sandboxing, that can foster innovation in AI-assisted healthcare, ensure patient safety, and inform on-going regulatory development.
The NHS should expand innovation acceleration programmes that bridge healthcare and industry partners, with a focus on increasing validation of AI products in real-world contexts and informing the development of a regulatory framework.
The Medicines and Healthcare Products Regulatory Agency and other Government bodies should arrange a post-Brexit agreement ensuring that UK regulations of medical devices, including AI-assisted healthcare, are aligned as closely as possible to the European framework and that the UK can continue to help shape Europe-wide regulations around this technology.
The General Medical Council, the Medical Royal Colleges, Health Education England, and AI-related bodies should partner with industry and academia on comprehensive examinations of the healthcare sector to assess which, when, and how jobs will be impacted by AI, including analyses of the current strengths, limitations, and workﬂows of healthcare professionals and broader NHS staff. They should also examine how AI-driven workforce changes will impact patient outcomes.
The Federation of Informatics Professionals and the Faculty of Clinical Informatics should continue to lead and expand standards for health informatics competencies, integrating the relevant aspects of AI into their training, accreditation, and professional development programmes for clinician-informaticians and related professions.
Health Education England should expand training programmes to advance digital and AI-related skills among healthcare professionals. Competency standards for working with AI should be identiﬁed for each role and established in accordance with professional registration bodies such as the General Medical Council. Training programmes should ensure that ”un-automatable” socio-emotional and cognitive skills remain an important focus.
The NHS Digital Academy should expand recruitment and training efforts to increase the number of Chief Clinical Information Ofﬁcers across the NHS, and ensure that the latest AI ethics, standards, and innovations are embedded in their training programme.
Legal experts, ethicists, AI-related bodies, professional medical bodies, and industry should review the implications of AI-assisted healthcare for legal liability. This includes understanding how healthcare professionals’ duty of care will be affected, the role of workforce training and product validation standards, and the potential role of NHS Indemnity and no-fault compensation systems.
AI-related bodies such as the Ada Lovelace Institute, patient advocacy groups and other healthcare stakeholders should lead a public engagement and dialogue strategy to understand the public’s views on liability for AI-assisted healthcare.