@@@@@

Posted in AI-assisted Cardiac MRI, 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 Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Conference Coverage with Social Media, COVID-19, Deep Learning in Pathology, Population Health Management, SARS-CoV-2, Scientific & Biotech Conferences: Press Coverage, Serology tests for coronavirus antibodies, Virus Infective Acute Respiratory Syndrome: SARS-CoV on May 12, 2020| Leave a Comment »

Expert moderators guiding discussions: 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

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

Dialogue among principals is a World Forum’s signature. Expert moderators guiding discussion and questions in audience friendly exchanges. No slides – shared perspectives facilitated by Harvard faculty, leading journalists and Mass General Brigham executives.

Jeffrey Golden, MD

Chair, Department of Pathology, BH; Ramzi S. Cotran Professor of Pathology, Harvard Medical School

Hadine Joffe, MD

Vice Chair, Psychiatry, Executive Director, Mary Horrigan Connors Center for Women’s Health and Gender Biology, BH; Paula A. Johnson Professor, Women’s Health, Harvard Medical School

Thomas Sequist, MD

Chief Patient Experience and Equity Officer, Mass General Brigham; Professor of Medicine and Health Care Policy, Harvard Medical School

Erica Shenoy, MD, PhD

Associate Chief, Infection Control Unit, MGH; Assistant Professor, Harvard Medical School

Gregg Meyer, MD

Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor, Harvard Medical School

Ravi Thadhani, MD

CAO, Mass General Brigham; Professor and Faculty Dean for Academic Programs, Harvard Medical School

Ann Prestipino

SVP; Incident Commander, MGH

Roger Kitterman

VP, Venture and Managing Partner, Partners Innovation Fund, Mass General Brigham

David Louis, MD

Pathologist-in-Chief, MGH; Benjamin Castleman Professor of Pathology, Harvard Medical School

Janet Wu

Bloomberg

Ron Walls, MD

EVP and Chief Operating Officer, BH; Neskey Family Professor of Emergency Medicine, Harvard Medical School

Alice Park

Senior Writer, TIME

Jeffrey Golden, MD

Chair, Department of Pathology, BH; Ramzi S. Cotran Professor of Pathology, Harvard Medical School

Hadine Joffe, MD

Vice Chair, Psychiatry, Executive Director, Mary Horrigan Connors Center for Women’s Health and Gender Biology, BH; Paula A. Johnson Professor, Women’s Health, Harvard Medical School

Thomas Sequist, MD

Chief Patient Experience and Equity Officer, Mass General Brigham; Professor of Medicine and Health Care Policy, Harvard Medical School

Erica Shenoy, MD, PhD

Associate Chief, Infection Control Unit, MGH; Assistant Professor, Harvard Medical School

Gregg Meyer, MD

Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor, Harvard Medical School

Ravi Thadhani, MD

CAO, Mass General Brigham; Professor and Faculty Dean for Academic Programs, Harvard Medical School

Ann Prestipino

SVP; Incident Commander, MGH

Roger Kitterman

VP, Venture and Managing Partner, Partners Innovation Fund, Mass General Brigham

David Louis, MD

Pathologist-in-Chief, MGH; Benjamin Castleman Professor of Pathology, Harvard Medical School

Janet Wu

Bloomberg

Ron Walls, MD

EVP and Chief Operating Officer, BH; Neskey Family Professor of Emergency Medicine, Harvard Medical School

Alice Park

Senior Writer, TIME

VIEW VIDEOS from the event

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

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

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

https://pharmaceuticalintelligence.com/2020/04/22/world-medical-innovation-forum-covid-19-ai-and-the-future-of-medicine-featuring-harvard-and-industry-leader-insights-mgh-bwh-virtual-event-monday-may-11-815-a-m-515-p-m-et/

Tweets & Retweets 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

https://pharmaceuticalintelligence.com/2020/05/11/tweets-retweets-2020-world-medical-innovation-forum-covid-19-ai-and-the-future-of-medicine-featuring-harvard-and-industry-leader-insights-mgh-bwh-virtual-event-mond/

Posted in AI-assisted Cardiac MRI, An executive's guide to AI, Artificial Intelligence - Breakthroughs in Theories and Technologies, 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, Artificial Intelligence in Psychiatry, Conference Coverage with Social Media, coronavirus, COVID-19, Population Health Management, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Serology tests for coronavirus antibodies, Virus Infective Acute Respiratory Syndrome: SARS-CoV on April 22, 2020| Leave a Comment »

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

12 views1 day ago

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

https://pharmaceuticalintelligence.com/2020/04/01/covid-19-and-ai-a-virtual-conference-human-centered-artificial-intelligence-stanford-university-4-1-2020-9am-pst-330pm-pst/

Tweets and Retweets @ COVID-19 and AI: A Virtual Conference – Human-Centered Artificial Intelligence Institute, Stanford University, 4/1/2020, 9AM PST – 3:30PM PST @StanfordHAI BY @pharma_BI and @AVIVA1950

COVID-19 and AI: A Virtual Conference – Human-Centered Artificial Intelligence Institute, Stanford University, 4/1/2020, 9AM PST – 3:30PM PST @StanfordHAI @pharma_BI @AVIVA1950

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

Aviva Lev-Ari

@AVIVA1950

15m

Quote Tweet

Aviva Lev-Ari

@AVIVA1950

· Apr 1

@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Fei-Fei Li AGE Fatality rate and infection rate of the aged Interaction between Acute Infection and Chronic Disease Safety of home – AI sensors at home Sensors data on secure systems clinically data recognized detection

Aviva Lev-Ari

@AVIVA1950

15m

Quote Tweet

Aviva Lev-Ari

@AVIVA1950

· Apr 1

@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Identifying COVID-19 Vaccine Candidates with ML Binbin Chen, MD and Ph.D. Student, Department of Genetics, Stanford University Immunogenic component of vaccine for COVID-19 spike protein bind epitome

Aviva Lev-Ari

@AVIVA1950

15m

Quote Tweet

Aviva Lev-Ari

@AVIVA1950

· Apr 1

@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Repurposing Existing Drugs to Fight COVID-19 Stefano Rensi #NLP Mine the literature for Proteins: Genomes genes proteins Biophysics #docking simulations for energy of 18 molecules as inhibitors Selection of candidate

Aviva Lev-Ari

@AVIVA1950

16m

Quote Tweet

Aviva Lev-Ari

@AVIVA1950

· Apr 1

@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po #ML can be helpful in critical care navigate complexity by automating processes vaccine mutations in the spike protein binding ACE2

Aviva Lev-Ari

@AVIVA1950

16m

Quote Tweet

Aviva Lev-Ari

@AVIVA1950

· Apr 1

@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po Mining article on sample size domain ares expert add to the challenges vs CS expertise alone

Aviva Lev-Ari

@AVIVA1950

16m

Michelle Kondrich and 9 others

Quote Tweet

Aviva Lev-Ari

@AVIVA1950

· Apr 1

@StanfordHAI @pharma_BI @AVIVA1950 pharmaceuticalintelligence.com/coronavirus-po #Virtual #informed #consent of #patient to accelerate ##clinical #trials

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Xavier Amatriain Lack accessibility to health care systems HC Accessibility and Scalability AI based HC IT System PDA – Personalized Diagnostics Assessment – for self reporting AI Automations + Physicians home testing

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Tina White, Ph.D. Candidate, Department of Mechanical Engineering, Stanford University China death toll >1000 China launched App to monitor quarantine early 1/2020 GPS based new App for contact tracing regulation on data

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po John Brownstein Late December 2019 collecting dat a HealthMap – public domain Baidu – has movement information connected with cases Temperature Data published Buoy data base customized to collect MA data on Temperature

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Jason Wang commend center in December 2019 All flight entering the country – Level 3 alert country: China Huhan, Hubei Quarantine all arriving from Level 3 alert country National STOKE PILES Activated x5 mask production

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Jason Wang Since 2003 Taiwan is preparing for a Pangemic JAMA paper on the topic is beebn reported Location of patient Taiwan National Epidemic Center 100 persons 24×7 in the Command Center Taiwan activated

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Michele Barry,TRACE together – Bluetooth tool on distance among people CHINA – contact racing surveillence scanning temp strict social distancing Hong Kong – tracing bracelets for quarantine Street locations of infected

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Michele Barry 5Million people travel out of Huhan Singapore – Free testing 1st country Temp testing stay at home, text phone from Authorities, show picture they are in quarantine for 5 days if negative TRACE together

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Seema Yasmin March 7, 2020 Italy news quarantine of 16 million lockdown large movement of people moving out of lockeddown areas, this movement based on information lead to spread of the viral spread

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Nigam Shah, Operational Planning – Utilization – Resource planning Clinical – who to test Research Questions – ACE2 receptors

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Congressman Ami Bera #Serological #testing represents ##diffusion in the #community and #immunity in communities #availability testing

@AbbottNews

contain #clusters #Diagnostics #positive #negative Immunity #Serological

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

Apr 1

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference

COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…

youtube.com

Aviva Lev-Ari

@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

Apr 1

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference

COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…

youtube.com

Aviva Lev-Ari

@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

Apr 1

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference

COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…

youtube.com

Aviva Lev-Ari

@AVIVA1950

pharmaceuticalintelligence.com/coronavirus-po Stanford Institute for Human-Centered Artificial Intelligence (HAI) Conference on COVID-19 and AI: A Virtual Conference on April 1, 2020 beginning at 9:00am (PDT). event covered in real time

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

You Retweeted

Stanford HAI

@StanfordHAI

57m

Using data science and design thinking,

@ronlivs

is building workflows enabled by machine learning to help hospitals care for their most critically ill patients. stanford.io/39zaGO2

Stanford HAI

@StanfordHAI

46m

Seniors are the age group most vulnerable to COVID-19.

@drfeifei

shares one way AI can help them stay safe. stanford.io/2UOjg64

0:23

541 views

Stanford HAI

@StanfordHAI

42m

Vaccines are one of the most powerful tools to curb a pandemic and prevent its recurrence,

@DrBinSquared

says. He discusses how AI tools built upon immunology knowledge and data can increase the chances of finding an effective vaccine. stanford.io/3aBidgh

Stanford HAI

@StanfordHAI

38m

Treatments for COVID-19 are urgently needed. “The fastest way to develop drugs is to repurpose existing drugs already on the market or in clinical trials.” – Stefano Rensi (

@TheRightStef

) stanford.io/3bIyJLF

#COVID_19 #COVID19 #FlattenTheCurve

Alvin

@alvie_barr

Mar 31

Important Read! Ventilator vs Respirator? Quarantine vs Isolation?

A comprehensive guide to the #COVIDー19 pandemic’s associated terms Read More: vox.com/science-and-he

Michelle Kondrich and 9 others

Aviva Lev-Ari

@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference

COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…

youtube.com

Aviva Lev-Ari

@AVIVA1950

Aviva Lev-Ari

@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Aviva Lev-Ari

@AVIVA1950

covering in real time Stanford HAI – COVID-19 and AI: A Virtual Conference youtu.be/z4105Exe23Q via

Stanford HAI – COVID-19 and AI: A Virtual Conference

COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…

youtube.com

Aviva Lev-Ari

@AVIVA1950

@StanfordHAI

@pharma_BI

@AVIVA1950

Coronavirus Portal

CORONAVIRUS PORTAL @LPBI Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact: Development of Medical Counter-measures for 2019-nCoV, Co…

pharmaceuticalintelligence.com

Posted in Advanced Computing Platform, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, 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 March 26, 2020| Leave a Comment »

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

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

https://bio-itworld.pathable.co/meetings/virtual/3T3SuWw9J2Bceei9s

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

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

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

https://pharmaceuticalintelligence.com/2020/10/08/tweets-retweets-by-pharma_bi-and-aviva1950-for-bioit20-19th-annual-bio-it-world-2020-conference-october-6-8-2020-in-boston/

October 6, 2020

PLENARY KEYNOTE –

10:15 am ET – NIH’s Strategic Vision for Data Science

Susan Gregurick

NIH

Associate Director for Data Science

Connected Data Ecosystem – Project is FAIR
Data shareable
NIH – agenda on data: diverse sets of data: Images of MRI, cells, of organs, of communities,
Share images and link it to tables
METADATA 34PB enable search – moving Data to clouds for Large-Scalable Analysis
Sequence Read Archive (SRA) – DNA seq.
COVID-19 from around the World SRA in Cloud Partnerships enabled
Open Science – enhance SW tools for making research cloud-ready
NIH has 12 Centers: Genomics, Neuro-imaging
SCH – Smart & Connected Health
IT, Sensor system hardware, effective usability, medical interpretation, Transformative data Science
Cancer, Alzheimer’s, Genomics, Medical Imaging, Brain circuits,
Coding it Forward: Students come to NIH Virtually from home to join CIVIL DIGITAL FELLOWSHIP
COVID-19: repositories of data for researches:

Treatment for Interventions
Long term Sequelae
Clinical platforms: BigData Catalyst, Allow US, ADSO, National COVID Cohort
Across platforms: workflow after RAS August Deploy: Passport for researchers to access data faster, Privacy-Preserving Tokens, Interoperability across clinical COVID data bases
Metadata super rich to link to other new data sources is a challenging issue to solve across studies

Scott Parker

Sinequa Corp

Director of Product Marketing

Disconnect between R&D & IT
Intelligence search Applications for sensitive information: Sinequa is a leader
shares one index cost for document go down & productivity increases

Rebecca Baker

NIH OD

Dir HEAL Initiative

END ADDICTION Project – NIH HEAL Initiative: 20 NIH collaborating on Studies
National Overdose Deaths overdose opioid drugs – synthetic Fentanyl
Heroin, Cocaine, Methamphetamine
During COVID Overdose increased during the pandemic
Increase in drug use overall and 67% of Fentanyl
Chronic Pain: Daily severe pain: can’t go to work – 25 Million
$500 Million/year Sustained Research Investment 25+ HEAL Research Programs
HEAL Initiative: Pain management, Translating research, New presention, enhance outcomes for affected newborns, novel medications options Pre-clinical translational research in Pain management
Improving treatments for opioid misuse & addiction
Opioid disorder people do not receive treatment: justice community, collaborative, ER, pregnant mothers
Medication-based treatment – do not stay long enough to achieve long-term recovery
People experience Pain differently: Muscular, neurological, : Biomarkers, endpoints, signatures, test non-addictive treatments for specific pains
Pain control balance of risks of long-term opioid therapy
HEAL Research – infant born after exposure to opioids in utero affect brain growth, born with withdrawal syndromes
Diversity of Data under HEAL Initiative –>> Harmonize the data
Common Data Elements in HEAL Clinical Research in Pain Management
CORE CDE & Supplemental CDE
Making HEAL Data FAIR: Findable, Accessible, Interpretable, Reusable
LINK HEAL data with communities studies, predict behaviours
Data sharing made available to the public
HEAL Data Lifecycle
effect of change due to change in dosage used – if dat is not collected – then we are not able to explore the relationships
Use the data to advance research beyond the current understanding of the problem
#NIHhealthInitiative

Ari Berman

BioTeam Inc

Chief Executive Officer

Distributed Questions from the Audience to the speakers

10:00 AM – 11:25 AM EDT on Tuesday, October 6

How to Hold on to Your Knowledge in an Agile World

Etzard Stolte

Roche Pharma

Global Head

October 7, 2020

The Chicagoland COVID-19 Commons: A Regional Data Commons Powering Research to Support Public Health Efforts

Matthew Trunnell

VP & Chief Data Officer

9:00 AM – 9:20 AM EDT on Wednesday, October 7

Seattle & COVID – samples from Seattle Flu Study
Public Health Practice vs Research – Data from Human Subjects: Avoid delute the control
Chicagoland COVID-19 Data Commons – in Chicago

Neighborhood level in Chicago
common data model
power efforts Predictive modeling : Case rate Total confirmed cases, Death cases
Legal agreement of the Consortium
https://chicagoland.pandemic

Commons – resources held in commons non-for profit
Data Commons: cloud based SW platforms that are co-located data, computing infrastructure and applications
Level 1: Basic, Level 2: Repeatable, Level 3: Governance Level4: Interoperability Level 5: Sustainable
COVID-19 Data Common: Public health authorities collects data – nor available to Research community
Research community need access to Public health authorities
Regional COVID-19 Data Commons: Reasons: Public health decision is LOCAL but specific to the Region
Fund raising in the communities
Data 1: Clinical Data for Health care Summary of incidence – Signals of ethnic dependencies and co-morbidities

Safe harbor: removal of 18 identifiers
Expert Determination

Data 2: Public Data: Environmental,
Data 3: Resident-Reported Data on iPhones: multiple languages supported early reports of people feeling unwell

CompBio: An Augmented Intelligence System for Comprehensive Interpretation of Biological Data

Richard Head

Washington Univ

Prof & Dir Genome Technology Access Ctr

9:20 AM – 9:40 AM EDT on Wednesday, October 7

Formating, data scrubbing,
Replace data fabric with simplified version
create “Memory Model” Machine learning does classification of patterns
dimensions are the variables
“Hyper-dimensional – ingestions of abstracts and articles
Example; IL^: Aggregate Memories to create a NORMALIZED Aggregate Memory
Relationships explored
Complex Knowledge Patterns Generated by the PCMM: Compared Utilization
Augmented AI System: Combination PCMM with AI
Literature mining CompBio
Evidence of Utility: PCMM – Accepted or Published Research Leveraging PCMM Applications
Example 1: Cell Metabolism CompBio – A person formulate hypothesis
Example 2: Analysis of RNA-Seq a rare mutational subtype of GBM

Hypothesis –>> BioExplorer –>> Multiple relations revealed
Example 3: Animal Models to Human Disease: CompBio – Crohn’s Assertion Engine

Summary – Augmented AI Platform for Biological DIscovery

PCMM – Memory modle – hyperdimensional
AAI Infrastructure
Knowledge map libraries
In development Medical Discoveries

PercayAI Team – commercial Development

Kingdom Capital

Precision Cancer Medicine

Jeffrey Rosenfeld

Rutgers Univ

Asst Prof

9:40 AM – 10:00 AM EDT on Wednesday, October 7

Cancer Classification: Shift from Anatomy/History of Molecular Etiology
Chronic Myeloenous Leukemia – Gleevec
Type cancer seq:

Hereditary cancer sequencing – BRCA
Tumor cancer sequencing

Panel Sizes – 500-1000x – the bigger the panel – more computational time more data need be investigated

Hotspot Panels,
Gene Panels,
Exomes

Cell free DNA Testing – Liquid biopsy

Apoptosis
Necrosis

FoundationONE
Patient Results: ALL mutations found, Mutation Burden,
Gene EGFR – no mutation
For every Mutation what Therapy is recommended for approved drugs
Clinical Trials for the mutations
VARIANTS of unknown significance
WORKFLOW: many MDs send sample get 38pps report
Genomic Classification and Prognosis in AML: Mutations subset and therapies available
Paradigm Shift in Classification

2013 – Lung Adenocarcinoma <<<- –
2011 – another cancer

mTOR System: A Database for Systems-Level Biomarker Discovery in Cancer

Iman Tavassoly – CANCELLED

C2i Genomics

Physician Scientist

10:20 AM – 10:40 AM EDT on Wednesday, October 7
Add to Calendar

FAIR and the (Tr)end of Data Lakes

Kees Van Bochove

The Hyve

Founder & Owner

10:20 AM – 10:40 AM EDT on Wednesday, October 7

Normalizing Regulatory Data Using Natural Language Processing (NLP)

Qais Hatim, Dr.

FDA CDER

Visiting Assoc

David Milward

Linguamatics

Senior Director, NLP Technology

10:40 AM – 11:00 AM EDT on Wednesday, October 7

ML focus on Disease
NLP – different words have same meanings, different expression same meaning, grammer & Meaning
Normalizes output

Disease
Genes
Dates
Mutations

Transform Unstructured into structured
Identifying Gaps in adverse events Labelling: Pain and Opioids
Improve drug safety
ChemAxon

Supplemental Approval Letters

Coding for Adverse events: “derived values of possible interest”

Use of Prominent Terminologies used at the FDA: UNII – Translation into ANSI tesaurus standard
Matching to the Variation found within Real Text: synonyms
Using ML for Normalization in Disease Context
Deep Learning PRE-TRAINING APPROACH for annotated date = supervised learning
A set of rules to handle overlapping entities
normalized the amp extracted from concepts
BERN and Terminologies: BioBERN, PubMed Central, PubMed Articles
NER – Named Entity Recognition
Evaluation of the Approach

Conclusions

NLP, ML, Hybrid methods, Terminology +ML methods

Building an Artificial Intelligence-Based Vaccine Discovery System: Applications in Infectious Diseases & Personalized Neoantigen-Related Immunotherapy for Treatment of Cancers

Kamal Rawal

Amity Univ

Assoc Prof

10:40 AM – 11:00 AM EDT on Wednesday, October 7

Classification of proteins
Data Collection
Feature Selection – Most important from 1447 features
Deep learning Model: Vaxi-DL: Layers, compilation
Overfitting Model strategy
Balancing Imbalanced
Hyper parameter tuning: Internal parameter of the model
Stratified K-Fold Training and Validation
Ensembling Approach: many weak classifier to create a STRONG Classifier
ROC Curve: Ensemble by Consensus
Before and after calibration
Benchmarking the system: Vaxi-DL Ensemble by Average vs by Consensus
SYSTEM developed: Type protein – find results
Rare disease CHARGE Syndrome was used for validation
Application to COVID-19 – Methodology
Application on Cancer: Which peptide can be used as antigen for prediction of immunogenic peptides

Using GPU Computing to Evaluate Variant Calling Strategies

George Vacek

NVIDIA Corp

Sequencing Strategic Development
Eriks Sasha Paegle

Dell EMC

Senior Business Development Manager

11:15 AM – 11:30 AM EDT on Wednesday, October 7

Navidia: 100 Genomes Cohort generated at NY Genome Center NHGRI
Navidia Parabricks mentioned AZURE
Dell EMC: Test environment: Dell Technology Cloud Storage for Multi-Cloud: resources across GCU, AWS, Azure in Northern Virginia regions
Multi-Cloud ease of use: without Multi-cloud vs with Faction multi-clouds
Ease of use
Deep Averaging Network (DAN)
NVIDIA CLARA PARABRICK TOOLKIT: Short & Long read, Deep learning, Data Analytics, ML
Reference applications – host of customized applications, 3rd Party App, Libraries
GPU (Genomics PUs) – Drop in tools for Somatic Pipelines : Clara Parabricks v3.5
Partnership of NVIDIA and Petagene announced at BioIT20 – NGS Data compretion
Petagene technology allows lossless compression reduce storage costs
Project with Sanger Institute – Optimizing Muto-graph Identification
completed run in 24 hours instead 31 days
Parabricks is a joint project Dell/EMC and NVIDIA

PLENARY KEYNOTE: Game On: How AI, Citizen Science, and Human Computation Are Facilitating the Next Leap Forward

12:30 PM – 1:55 PM EDT on Wednesday, October 7

Allison Proffitt

BioIT World & Diagnostics World

Editorial Dir

Seth Cooper

Northeastern Univ

Asst Prof

Foldit – Scientific discovery using video games in the domain of protein structures and folding
Combine Human with machine
Score based on competition among players for higher score and collaboration in groups
Problem: Chemistry give input.
Puzzle available for one week on the Internet, games ongoing,
Solution analysis – continually IMPROVE the structure of Protein folding
Foldit Tutorials offered online
Player accomplishments: Articles by scientists ,
development of algorithms discovery
Electron Density fitting
Enzyme re-design
de novo Protein Design – named authors on a paper – scientific process
Future Work: Coronovirus Spike protein
Small molecule design
narrative
virtual reality – 3D protein structure for manipulation
htp://Fold.it/Educator Mode
htp://Fold.it/standalone
http://fold.it/
seth.cooper@gmail.com

Lee Lancashire, CIO

Cohen Veterans Bioscience – not for profit – advancing Brain health

Biotyping and stratification
Biomarkers
Omics data
All meet in the Common – Brain Commons: Clinician, Geneticist, Scientist, Bioinformatician, R Studio, Python, Jupyterhub
Multidimensional Biomarkers in Multiple Sclerosis

Pietro Michelucci

Human Computation Institute

Director

Why machine can’t tackle AI on their own and AI can’t do Precision Medicine on their own
young people more than others N of 1 – Precision Mediicne
Scandinavians and Russians are immune
AI & Precision Medicine: can’t solve the complexity of messy data vs big data
Messy data: heterogeneous multidimensional, to many combinations to explore, select which combination to explore vs let the machine generate all the combination and do analysis on all and discover PATTERN
Causal vs spurious
Logical reasoning, right brain abstract and short cuts – Human brain does routinely
Human do better on context: Not all info is in pixels such as context
#ADS – SBIR suspected the hypothesis to be tested
improving crowd wisdom methods: 20 input by different people PLUS machine
combine crowd answers with machine faster and improved accuracy
Machine has no intuition – machine bias of Human and of machine is similar
Wisdom of Crowd: Bootstrapping hybrid Intelligence: CIVIUM
bit.ly/civiumintro

Jerome Waldispuehl

McGill Univ

Assoc Prof

visualization of nucleotide – tools for
http://phylo.cs.mcgill.ca
GAME: Phylo DNA Puzzles: Goal 202, Score, Top Score
Whole-genome multiple
Phylo: 350,000 participants, 1MM solutions Improve 40 to 95% computer alignments
education & science outreach – reach out to the Public
Borderlands Science + game designers: 1MM participants 50MM solutions
Joint initiative with a major science project
Improvement of 16S rRNA
MMOS company in Science games

Towards AI-Guided Cell Profiling of Drugs with Automated High-Content Imaging

Ola Spjuth

Uppsala Univ

Professor

2:10 PM – 2:30 PM EDT on Wednesday, October 7

Accelerate drug discovering using AI automation in collaboration with AstraZeneca
Closed-loop (autonomous) experimentation
collect the best data at the minimal cost
Active learning: query active learning model
Exploitation [best predictions from given data] vs Exploration
Automation in Life Science: micro-plate, stack of micro-plates
Robot scientist: come out with hypothesis and conduct research
high-throughput biology: Robots vs Disease
Cell painting: Imaging with multiplexed dyes: genetic or chemical perturbations
classify images into biological mechanisms
combinations of toxicants
A discovery engine: Toxicity, Efficacy, mechanisms combinations
Automating our cell-based lab: fixed setup
Open source lab automation suite: Github https://github.com/pharmbio/imagedb
Dealing with large scale data [TensorFlow]
STACKn.com – AI modeling Life cycle
HASTE: Hierarchical analysis of Spacial and Temporal
https://pharmb.io

Advanced Imaging and AI Technologies Providing New Image and Data Analysis Challenges and Opportunities

Richard Goodwin

AstraZeneca

Dir & Head of Imaging & AI

2:30 PM – 2:50 PM EDT on Wednesday, October 7

AstraZeneca is empowering its scientists to see the complexity of a disease in unprecedented detail to enable effective development and selection of new medicines. This is enabled though the use of an extensive range of cutting-edge imaging technologies that support studies into the efficacy and safety of drugs through the R&D pipeline. This presentation will introduce the range of novel in vivo and ex vivo imaging technologies employed, describe the data challenges associated with scaling up the use of molecular imaging technologies, and address the new data integration and mining challenges. Novel computational methods are required for large cohort imaging studies that involve tissue based multi-omics analysis, which integrate spatial relationships in unprecedented detail.

Small molecule – not suitable for complex diseases
focus on quality vs quantity
compound for commercial value
right safety
Imaging supports R&D: Molecular, medical, big data and AI
convergence of ML for decision making
Spatial imaging: morphology
Multiplex imaging like MRI
Multimodal analysis: tissue data and invivo holistic understanding of drug delivery
spacial transcriptomics proteomics: imaging platforms in R&D
AZ invest in imaging technologies already impacting projects: AI-empowered imaging delivering subcellular resolution
Mass Spec Imaging (MSI) – ex-vivo imaging techniques- spatial distribution of molecular
cartography of cancer: Drug metabolite distribution – NEW understanding of disease and drug distribution in tissue
DATA: digitization, integration, analysis, exploration
Digital pathology and beyond – AI Image Analysis – AI outperform pathololigst and radiologists
Data volume and dimensionality challenge and opportunity
Data volume and dimensionality: complete image
AZ Oncology – disease is understood for drug discovery using Imaging technology

PANEL: Framework and Approach to Unlock the Potential of Quantum Computing in Drug Discovery

Brian Martin

AbbVie Inc

Research Fellow & Head

Philipp Harbach

Merck KGaA

Head of In Silico Research in Germany

chemistry and manufacturing with QC – end user in Pharmaceutical
VC at Merck ask expert in Merck to guide investment of Merck in QC
50 people across Merck [three areas at Merck [Pharmaceutics, Animal Health, Diagnostics]

Celia Merzbacher

SRI Intl

Assoc Dir Quantum Economic Dev Consortium (QEDC)

Methodology from Pistoia to be used in QC
QC R&D developed in parallel
Simulation of all the components is possible

John Wise

Pistoia Alliance Inc (2007)

We are a global, not-for-profit members’ organization working to lower barriers to innovation in life science and healthcare R&D through pre-competitive collaboration.

Consultant

How Pharmaceutical Industry can benefit from quantum computing
9 of 10 big Pharma are members of the Pistoia Alliance
IP created on specifications

Zahid Tharia

Pistoia Alliance Inc

Consultant

Barriers to adoption of quantum computing (QC) in Pharma is training of staff and skills in the IT aspects of QC

3:10 PM – 4:00 PM EDT on Wednesday, October 7

TRACK 11: BIOINFORMATICS TRACK 12: PHARMACEUTICAL R&D INFORMATICS

Session Wrap-Up Panel Discussion

Etzard Stolte, PhD

Roche Pharma

Global Head

no official policy
2020 it become important to be mentioned by management as a potential use in automation
continual updates needed – it is manual and a disillusion without a business case
Roche try to commodatized tools in AI as Classifiers, automation,

Samiul Hasan

GlaxoSmithKline

Scientific Analytics and Visualization Director

AI is perceived as having potential to take off on its own
POC – demonstrate the vlaue
Proof of Concept – Semantic report – a story vs one off
demonstration of value is needed and is continuous

Bin Li

Millennium The Takeda Oncology Co

Dir Computational Biology & Translational Medicine

ML community at Takeda
Positive to have, how successful not much yet – not used much yet
some models are pretty good do not need improvement

Jens Hoefkens

Accenture

Industry Principal Director

Future of AI as support to the Human intuition vs replacement of humans
automation like pathology classification
Machine and Human working together – not as maker of decisions in clinical settings
POC cycle prevent production conversion
where is the highest value for production and deploy with scale
AI Assisted to sift Genomics data
BERT term extraction from Google technology to make sense of data assist the user
ML
RPA – Robotic concept extraction – 80% accuracy needed by scientists

4:00 PM – 4:20 PM EDT on Wednesday, October 7

October 8, 2020

Trends from the Trenches

Kevin Davies, PhD

CRISPR Journal

Exec VP & Exec Editor

Timothy Cutts

Wellcome Sanger Institute

Head

Collaborations with scientists in subSahara
pay for data analysis – ownership issues
in UK 6 Labs for the entire countries: all send the data to Wellcome Sanger Institute for analysis
Metadata is the problem – coordination of each of the 6 labs to send the metadata created problems

Cindy Crowninshield

Cambridge Healthtech Institute

Executive Event Director

Vivien Bonazzi

Deloitte Consulting LLP

Managing Dir & Chief Biomedical Data Scientist

How organizations use bioscience data
Data Ecosystem: Hardware and software: Cloud and other options
Operationalize the two trends:

Platforms: End to end solutions resulting in SILOS, systems are native: data ingestions
Data Commons: Open arch, open source – integration and interdependence issues

Biomedical Agencies in NIH various Organizations in the Private sector: Sharing data must be more effective
IT, Data Science, Management – COVID – reduced barriers
Leadership: Different voices from different people
Data strategies & Governance not the whole but small pieces , incentives to share data

Chris Dagdigian

BioTeam Inc

Sr Dir

10th Anniversary to Trends from the Trenches
IT infrastructure changes
Research IT:

Genomics & BioInformatics
Image-based data acquisition and analysis: CryoEM, 3D microscopy, fMRI image analysis
ML and AI – GPU FPGAs, neural processors: Drive in organizations: bottom up
Chemistry & Molecular Dynamics
Storage and exploitation of data for insights
2020 Hype vs Reality
Scientific Data: managing and understanding, data movement, federated/access
Big Data: data storage, management & governance standards vs human curated data
IT needs guidance and decisions from Science Team
Culture change for joint management by Science & IT: data fidelity, attribution, allocation top down
NERSC File System quotas & Purging overviewSilos & So
Petabytes of open access data, collaborative research resources: Data rich environments
Data Lakes: Gen3 Data Commons
Data hygiene:metadata is Science side vs IT
Biased Data: Model & Data Bias

Failed Predictions:

Compilers matter again – not True
CPU benchmarking is back – WRONG
AMD vs Inter arm64 vs both
Policy driven auto-tiering storage – wrong, USER self-service for tiering, movement and archive decision. Let researchers tier/move/archive based on Project, Experiment or Group
Single storage namespace – Wrong: Data intensive science: scientists must do some IT jobs themselves

Kjiersten Fagnan

Lawrence Berkeley Natl Lab

CIO

Genome Project of DOE
Data management with other agencies
COVID: Collaborations, breaking down barriers, small labs and big labs ALL generate data and sharing
that collaboration is needed regardless of COVID – not happen
If twoo big one lab can’t handle it all
Funding and training does not support the Collaborations because next round of funding depend on individual publications – which requires silos
Data cleaning and data management:Standards are annoying and painful – not needed for publishing the results as soon as possible – just that someone else will be able to use it
Facebook have hundred of curators – the curation of scientific data requires same hunsrands od curators that are SCIENTISTS and Data scientists

Matthew Trunnell

Pandemic Response Commons, Seattle

VP & Chief Data Officer

Data commons for intra- and inter-mural data sharing
ML is needed for Data commons
Progress in FAIRness, NIH efforts driven by Susan Gregory across NIH all centers
Large amount of B-to-B Data sharing UBER sharing with a jurisdiction they operate
SNOWFLAKES – new cloud technology
COVID – plays an accelerator
Cancer vs COVID – transfer knowledge from COVID to Cancer

9:00 AM – 10:40 AM EDT on Thursday, October 8

The “Trends from the Trenches” will celebrate its 10th Anniversary at Bio-IT! Since 2010, the “Trends from the Trenches” presentation, given by Chris Dagdigian, has been one of the most popular annual traditions on the Bio-IT Program. The intent of the talk is to deliver a candid (and occasionally blunt) assessment of the best, the worthwhile, and the most overhyped information technologies (IT) for life sciences. The presentation has helped scientists, leadership, and IT professionals understand the basic topics related to computing, storage, data transfer, networks, and cloud that are involved in supporting data-intensive science. In 2020, Chris will give the “Trends from the Trenches” presentation in its original “state-of-the-state address” followed by guest speakers giving podium talks on relevant topics. An interactive Q&A moderated discussion with the audience follows. Come prepared with your questions and commentary for this informative and lively session.

Q&A

Project vs enterprise – Sequencing for internal research vs for clients’ data
Tension in governmental agencies – no robust solutions: IT, Science, Management
different Use cases need different infrastructure: HW & SW: Storage and data exploration
Data Lakes: rule base, enterprising – training is an issue in organizations
Management, Scientists, IT in enterprises – terra byte of storage, budgets issues, conversation on the limits that IT can ofer putting more burden on the Scientists for triage and quotas – business and scientific value
New capabilities in organizations: hands on in data management tactical of data management not IT bur data engineering
Citizen Science: privacy vs plants and microbes – no privacy issues
Incentives need be changed for Data Citations in addition to Papers
Curation Citations as Authorship citation
Data sharing in Cancer: GEN3 – NCI Data Commons, Data Governance and Data Permission (Access) – NCI does work in data commons – much data outside this space
EBI – in UK Sanger Institute has the infrastructure in one place
Migrating Project based Data structure: that involves scientist decisions that should not be a quota (storage is full) in the IT space
Human to Human communications vs tools for data migration
Which Organizations get the data curation and annotation well: Subject matter from day 1 – hard to teach vs data engineering skills; TEAM as a solving is critical in Biomedical space no incentives
BBC – Meta tagging system is outstanding
NCAST TRANSLATOR – across organizations
Changing incentives – MORE organizations will do that task better
Common metadata across domains with predict uses of data in the Future – collaboration of CS to create in the science organization tagging like in BBC

TRACK 16: OPEN ACCESS AND COLLABORATIONS TRACK 15: CANCER INFORMATICS TRACK 13: GENOME INFORMATICS

Session Wrap-Up Panel Discussion

Chris Anderson

Clinical OMICs

Editor in Chief

Ian Fore

NIH NCI

Sr Biomedical Informatics Program Mgr

NCI – Cancer Data Commons – concierge services to organization on data services

Ravi Madduri – CVD large cohort

Univ of Chicago

Scientist

Lara Mangravite

Sage Bionetworks

President
Kees Van Bochove

The Hyve

Founder & Owner

11:10 AM – 11:30 AM EDT on Thursday, October 8

BREAKOUT: Driving Scientific Discovery with Data / Digitization

Timothy Gardner

Riffyn Inc

CEO

11:35 AM – 12:00 PM EDT on Thursday, October 8

PLENARY KEYNOTE – 12:00 PM – 1:25 PM EDT on Thursday, October 8

Robert Green

Brigham & Womens Hospital

Co-founder of Genome Medicine

Prof & Dir G2P Research

Combining data to rapidly analyze COVID-19 Patients –
identify BIOMARKERS for vulnerability
Preventive Genomics – Angelina Jolly’s musectomy as a preventive clinical condition
Patients access to own genomics data
Population screening – to predict risks
Genetic Testing to Consumer: Preventive Genomics: conflated genotyping/sequencing and labs/care providers
Genetic Testing to Consumer: COST & Benefits – UNCLEAR

diagnosis of unsuspected genetic disease
stratification for surveillance
which pieces of the puzzle need to be brought to bear in patient care
Categories and Reporting criteria: Gene-Disease validity vs Variant Pathogenicity –>> Clinic
MedSeq Project: 10MM randomized study – all genome info shared with Patient, other arm only selective genome data shared with patient: 100 patients 20% carry monogenic condition: Polygenic risk scores:
CAD – high Cholesterol biomarker, A-FIb, DM2, 52% Women 48% Men
No high risk error by PCP discussing and disclosing the results of the sequence
Filtering the results: Indication -based testing vs Screening
BabySeq Project: INFANTS sequencing to prevent disease: 11% carry a mutation in a monogenic gene for a monogenic condition -like abnormal narrowed aorta
MDR – Monogenic Disease Risk
MilSeq Project: US Air Force – Military active duty
5,8,10 – are all Polygenic studies
Polygenic Risk Scores – High risk
Classification need to be repeated every few years (2 years – re-sequence) due to changes in health and to efficiencies in new discovery in curated data which is improving as on-going

Risk benefit – UTILITY – Partners Biobank Return of Genomic Results
No interest on knowing by the Public NCCN criteria on chart review 20%
Brigham Preventive Genomics via telemedicine – First in the country
APC mutation after colonoscopy – obstruction diagnosed
www.genomes2people.org
@robertgreen

Juergen Klenk

Deloitte Consulting LLP

Principal

Bradykinin hypothesis for COVID-19
liberate the data: People , Data Risk

Natalija Jovanovic

Sanofi

Chief Digital Officer

AI in Pharma
Vaccine preventable diseases – produce 1Billion vaccines a year

reduction of incidence: Pertusis – 92% eradication

manage risk profile
Science mechanism translatable to machines

high automated ingestible data for AI
Digital is about people: Good data Good algorithms Good GUI

Vivien Bonazzi

Deloitte Consulting LLP

Managing Dir & Chief Biomedical Data Scientist

12:00 PM – 1:25 PM EDT on Thursday, October 8
Add to Calendar

12:00 Organizer’s Remarks

Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute

12:05 Keynote Introduction

Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP

12:15 Toward Preventive Genomics: Lessons from MedSeq and BabySeq

Robert Green, MD, MPH, Professor of Medicine (Genetics) and Director, G2P Research Program/Preventive Genomics Clinic, Brigham & Women’s Hospital, Broad Institute, and Harvard Medical School

12:40 AI in Pharma: Where We Are Today and How We Will Succeed in the Future

Natalija Jovanovic, PhD, Chief Digital Officer, Sanofi Pasteur

1:05 LIVE Q&A: Session Wrap-Up Panel Discussion

PANEL MODERATORS:

Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP

Vivien R. Bonazzi, PhD, Managing Director & Chief Biomedical Data Scientist, Deloitte Consulting LLP

1. PLENARY KEYNOTE PRESENTATION

10:15 am ET – NIH’s Strategic Vision for Data Science

Susan K. Gregurick, PhD, Associate Director, Data Science (ADDS) and Director, Office of Data Science Strategy (ODSS), National Institutes of Health

Rebecca Baker, PhD, Director, HEAL (Helping to End Addiction Long-term) Initiative, Office of the Director, National Institutes of Health

2. WORKSHOPS

11:55 am ET – W1: Data Management for Biologics: Registration and Beyond

Monica Wang, PhD, Principal Technology Lead, Scientific Informatics, Takeda

Sebastian Schlicker, Head, Biologics Business Operations, Genedata AG

11:55 am ET – W2: A Crash Course in AI: 0-60 in Three

Peter V. Henstock, PhD, Machine Learning & AI Lead, Software Engineering & Statistics & Visualization, Pfizer Inc.

11:55 am ET – W3: Data Science Driving Better Informed Decisions

Meghan Raman, Director, R&D Data Lake & Analytics, Bristol Myers Squibb Co.

Nigel Greene, PhD, Director & Head Data Science & Artificial Intelligence, Drug Safety & Metabolism, AstraZeneca Pharmaceuticals

2:15 pm ET – W4: Digital Biomarkers and Wearables in Pharma R&D and Clinical Trials

Danielle Bradnan, MS, Research Associate, Digital Health and Wellness, Lux Research

Graham Jones, PhD, Director, Innovation, Technical Research and Development, Novartis

Ariel Dowling, PhD, Director of Digital Strategy, Data Sciences Institute, Research and Development, Takeda Pharmaceuticals

2:15 pm ET – W5: AI-Celerating R&D: Foundational Approaches to How Emerging Technologies Can Create Value

Brian Martin, Head of AI, R&D Information Research, Senior Principal Data Scientist, AbbVie

2:15 pm ET – W6: Dealing with Instrument Data at Scale: Challenges and Solutions

Rachana Ananthakrishnan, Executive Director, Globus, University of Chicago

Michael A. Cianfrocco, PhD, Assistant Professor, Department of Biological Chemistry and Research Assistant Professor, Life Sciences Institute, University of Michigan

Brigitte E. Raumann, Product Manager, Globus, University of Chicago

3. Connect with peers from across the industry during these dedicated networking times.

9:25 am ET – Virtual Exhibit Hall Open

1:00 pm ET – Speed Networking

Looking to meet fellow attendees and have meaningful conversations – just as you would at an in- person event? This is the perfect way to achieve just that. Get to know your fellow attendees by joining this interactive speed networking event. To participate, each attendee will be paired at random with another fellow attendee and given a chance to interact for 7 minutes in a private zoom room. Once the 7 minutes are up, you will move on to meet with another selected attendee. Maximize your networking at the meeting and join in.

2:00 pm ET – Stretch Break

Take a minute to revitalize and join our friends from VOS Fitness for a stretch break. The professional trainer from VOS will bring you through some easy moves that will help with screen fatigue and ease your muscles after a long day of sitting at the computer. All moves can be done right at your desk and is appropriate for all fitness levels.

4. Game On!

Earn points by completing the activities listed on our Game tab. Some activities will only award points once, but others will award you every time you do it – so the more involved you are in the virtual event, the more points you will earn! You can start earning points one week before the event – so get ready to start sending meeting invitations, exploring our virtual expo and planning your schedule.

Attendees in the top 5% of points earned when the game closes at the end of the conference will be eligible to win a gift card worth $200 USD!

5. Take part in 1-on-1 networking with an easy-to-navigate profile search and scheduling platform.

Check out your recommended connections flagged as “Want to Meet” in the People Tab. These connections were chosen based on your similar roles, companies and conference program interests.
Take a moment to add relevant interest tags to your profile. Then search and connect with participants who have the same interests.
Engage with technology leaders in their booths and view relevant videos and demos.
Take part in live Q&A with speakers and participants following each educational session.
Create and join in ad hoc group discussions throughout the event.
Watch Our Quick Tutorial on how to Maximize Networking Opportunities: CII’s Virtual Event Platform – Networking

10:00 AM – 11:25 AM EDT on Tuesday, October 6
Add to Calendar

Wednesday, October 7

9:00 AM EDT

TRACK 7: AI FOR DRUG DISCOVERY

The Emergence of the AI-Augmented Drug Discoverer

9:00 AM – 9:20 AM EDT

PRESENTATIONON DEMANDRECORDEDSESSION PASS

Mark Davies

BenevolentAI

9:20 AM EDT

TRACK 7: AI FOR DRUG DISCOVERY

Generative Chemistry and Generative Biology for AI-Powered Drug Discovery

9:20 AM – 9:40 AM EDT

PRESENTATIONON DEMANDRECORDEDSESSION PASS

Alex Zhavoronkov

Insilico Medicine

9:40 AM EDT

TRACK 7: AI FOR DRUG DISCOVERY

Talk Title to be Announced

9:40 AM – 11:00 AM EDT

PRESENTATIONON DEMANDRECORDEDSESSION PASS

Grace Wenjia You

EMD Serono

11:00 AM EDT

TRACK 7: AI FOR DRUG DISCOVERY

Coupling AI and Network Biology to Generate Insights for Disease Understanding and Target ID

11:00 AM – 11:30 AM EDT

PRESENTATIONON DEMANDRECORDEDSESSION PASS

Alexander Ivliev

Clarivate

11:30 AM EDT

TRACK 7: AI FOR DRUG DISCOVERY

Session Wrap-Up Panel Discussion

11:30 AM – 11:50 AM EDT

PANELON DEMANDLIVESESSION PASS

@@@@@

OLD Material

http://www.giiconference.com/chi909998/

Welcome to Bio-IT World 2020

In the spirit of open collaboration, the world’s premier bio-IT conference will bring together the community to focus on how we are using technologies and analytic approaches to solve problems, accelerate science, and drive the future of precision medicine. With a focus on AI, data science and other “data-driven” technologies that are advancing biomedical research, drug discovery and healthcare, the Bio-IT World Conference & Expo ’20 will bring together more than 3,000 participants to the Seaport World Trade Center in Boston from October 6-8, 2020.

The participants will have the chance to meet and share research/ideas with leading life sciences, pharmaceutical, clinical, healthcare, informatics and technology experts.

BROCHURE

http://www.giiconference.com/chi909998/catalog.pdf?20200122

2020 CONFERENCE PROGRAMS VIEW

TRACK 1 Data Storage and Transport VIEW

TRACK 2 Data and Metadata Management VIEW

TRACK 3 Data Science and Analytics Technologies VIEW

TRACK 4 Software Applications and Services VIEW

TRACK 5 Data Security and Compliance VIEW

TRACK 6 Cloud Computing VIEW

TRACK 7 AI for Drug Discovery VIEW

TRACK 8 Emerging AI Technologies VIEW

TRACK 9 AI: Business Value Outcomes VIEW

TRACK 10 Data Visualization Tools VIEW

TRACK 11 Bioinformatics VIEW

TRACK 12 Pharmaceutical R&D Informatics VIEW

TRACK 13 Genome Informatics VIEW

TRACK 14 Clinical Research and Translational Informatics VIEW

TRACK 15 Cancer Informatics VIEW

TRACK 16 Open Access and Collaborations

2020 Plenary Keynote Speakers

Rebecca Baker, PhD

Director, HEAL (Helping to End Addiction Long-term) Initiative, Office of the Director, National Institutes of Health

Vivien Bonazzi, PhD

Chief Biomedical Data Scientist, Managing Director, Deloitte

Tim Cutts, PhD

Head, Scientific Computing, Wellcome Trust Sanger Institute

Chris Dagdigian

Co-Founder and Senior Director, Infrastructure, BioTeam, Inc

Kevin Davies, PhD

Executive Editor, The CRISPR Journal, Mary Ann Liebert, Inc.

Kjiersten Fagnan, PhD

Chief Informatics Officer, Data Science and Informatics Leader, DOE Joint Genome Institute, Lawrence Berkeley National Laboratory

Robert Green, MD, MPH

Professor of Medicine (Genetics) and Director, G2P Research Program/Preventive Genomics Clinic, Brigham & Women’s Hospital, Broad Institute, and Harvard Medical School

Susan K. Gregurick, PhD

Associate Director, Data Science (ADDS) and Director, Office of Data Science Strategy (ODSS), National Institutes of Health

Natalija Jovanovic, PhD

Chief Digital Officer, Sanofi Pasteur

Pietro Michelucci, PhD

Director, Human Computation Institute

Matthew Trunnell

Vice President and Chief Data Officer, Fred Hutchinson Cancer Research Center

3,200+

Industry
Professionals

160+

Sponsors &
Exhibitors

250+

Scientific
Presentations

Diverse
Conference Tracks

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

https://www.digitalpulse.pwc.com.au/top-tech-trends-debate-2019/

The Future of Synthetic Biology

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Biological Engineering, tagged Artificial intelligence, Global Market, Materials, Synthetic biology on December 25, 2019| Leave a Comment »

The Future of Synthetic Biology

Reporter: Irina Robu, PhD

With an estimated global evaluation of around $14 billion US, synthetic biology is a rapidly accelerating market. Nonetheless while the growth of the market has been remarkable, the ttrue impact has not yet been seen. The era of AI will quickly increase the pace of discovery, and produce materials not seen in nature, through extrapolation and generative design. The extraordinary is now possible: producing spider silk without spiders, egg proteins without chickens and fragrances without flowers.

Synthetic biology companies are associating with fashion designers as well as forming ‘organism foundries. Rapidly, AI will utilize its learning of the natural world to make guided inferences which produce entirely new materials. From a technology perspective, we’re experiencing an explosion of capability that will be invasive in the next 3-5 years. Language models have come a long way, to the point where full models are being kept private so as not to endanger the public.

Already today, the average person has the ability to start their own commercial space venture for less than the cost of a juice franchise. PwC Australia’s Charmaine Green believes secret trends can hide among obvious ones. She outlines three trends leading to her hypothesis that Australia is well placed to become the global creative hub for video game development.

Economies like Australia are situated to capitalize on this trend, and video game development can become a permanent and substantial part of the economy. In Australia, Green argues, we have all the basic elements needed: high ingenuity, creative risk taking, and the freedom and flexibility that comes with the country’s small-to-mid studios.

SOURCE

Posted in AI-assisted Cardiac MRI, 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, Circumferential-Intravascular-Radioluminescence-Photoacoustic-Imaging (CIRPI), Image Processing/Computing, Medical Imaging Technology, Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound, MRI, Noninvasive Diagnostic Fractional Flow Reserve (FFR) CT, Ultra Sound on December 17, 2019| Leave a Comment »

Artificial Intelligence Innovations in Cardiac Imaging

Artificial Intelligence Innovations in Cardiac Imaging

Reporter: Aviva Lev-Ari, PhD, RN

3.3.23

3.3.23 Artificial Intelligence Innovations in Cardiac Imaging, 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

‘CTA-for-All’ fast-tracks intervention, improves LVO detection in stroke patients

Anicka Slachta | November 15, 2019 | Cardiovascular Imaging

A “CTA-for-All” stroke imaging policy improved large vessel occlusion (LVO) detection, fast-tracked intervention and improved outcomes in a recent study of patients with acute ischemic stroke (AIS), researchers reported in Stroke.

“Combined noncontrast computed tomography (NCCT) and CT angiography (CTA) have been championed as the new minimum standard for initial imaging of disabling stroke,” Mayer, a neurologist at Henry Ford Hospital in Detroit, and co-authors wrote in their paper. “Patient selection criteria that impose arbitrary limits on time from last known well (LKW) or baseline National Institutes of Health Stroke Scale (NIHSS) score may delay CTA and the diagnosis of LVO.”

“These findings suggest that a uniform CTA-for-All imaging policy for stroke patients presenting within 24 hours is feasible and safe, improves LVO detection, speeds intervention and can improve outcomes,” the authors wrote. “The benefit appears to primarily affect patients presenting within six hours of symptom onset.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/cta-all-fast-tracks-intervention-improves-lvo-detection-stroke?utm_source=newsletter&utm_medium=cvb_cardio_imaging

How to integrate AI into the cardiac imaging pipeline

Anicka Slachta | December 05, 2019 | Cardiovascular Imaging

Hsiao said physicians can expect “a little bit of generalization” from neural networks, meaning they’ll work okay on data that they’ve never seen, but they’re not going to produce perfect results the first time around. If a model was trained on 3T MRI data, for example, and someone inputs 1.5T MRI data, it might not be able to analyze that information comprehensively. If some 1.5T data were fed into the model’s training algorithm, though, that could change.

According to Hsiao, all of this knowledge means little without clinical validation. He said he and his colleagues are working to integrate algorithms into the clinical environment such that a radiologist could hit a button and AI could auto-prescribe a set of images. Even better, he said, would be the ability to open up a series and have it auto-prescribe itself.

“That’s where we’re moving next, so you don’t have to hit any buttons at all,” he said.

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/how-integrate-ai-cardiac-imaging-pipeline?utm_source=newsletter&utm_medium=cvb_cardio_imaging

DiA Imaging, IBM pair to take the subjectivity out of cardiac image analysis

Anicka Slachta | December 05, 2019 | Cardiovascular Imaging

IBM Watson Health is adding startup DiA Imaging Analysis to its AI Marketplace in an effort to offer clinicians access to more objective and accurate ultrasound analysis, the company announced Dec. 1.

DiA, an IBM Alpha Zone Accelerator Alumni Startup, has developed AI-powered cardiac ultrasound software that’s already been cleared by the FDA. According to a release, the software was designed to help physicians analyze cardiac ultrasound images automatically and more objectively, since image interpretation is inherently a somewhat subjective process.

“Our collaboration with IBM Watson Health demonstrates the implementation of DiA’s vision to make the analysis of ultrasound images smarter and accessible to clinicians with various levels of experience on any platform,” DiA CEO and co-founder Hila Goldman-Aslan said in a statement.

IBM will focus specifically on DiA’s LVivo EF solution, an application with an AI-based quantification solution that provides clinicians with automated clinical data like ejection fraction and global longitudinal strain.

“IBM Watson Health is proud to announce a collaboration with DiA Imaging,” Anne Le Grand, general manager of imaging, life sciences and oncology at IBM, said. “DiA’s innovative AI-powered offerings can provide our clients with the ability to analyze images with advanced AI-based solutions which can support IBM Watson Health’s mission to help build smarter ecosystems.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/dia-imaging-ibm-partner-cardiac-image-analysis?utm_source=newsletter&utm_medium=cvb_cardio_imaging

FDA clears Ultromics’ AI-based CV image analysis system

Anicka Slachta | November 18, 2019 | Cardiovascular Imaging

U.K.-based health tech firm Ultromics has secured 510(K) FDA clearance for its EchoGo Core image analysis system, the company announced Nov. 14.

EchoGo leverages artificial intelligence to calculate left ventricular ejection fraction, LV volumes and automated cardiac strain on ultrasound-based heart scans. The idea, founder and CEO Ross Upton said, is to automate the analysis and quantification of echos so cardiologists can make more informed decisions about care delivery.

“This is an incredibly exciting step toward the future of healthcare,” Upton, a Forbes “30 Under 30” honoree this year, said in a statement, calling the 510(K) clearance “truly a watershed moment” for his company.

Notably, the FDA’s choice to clear Ultromics’ technology means it will be available to a wider population of patients and providers. Based in the U.K., the company has only been independent of the University of Oxford for two years.

Upton said the EchoGo system will make Ultromics the first tech company to use AI for automated strain analysis, which is applicable to some 60 million scans per year and will be reimbursable in the U.S. starting in January. He said EchoGo could be a useful tool for physicians of all experience levels looking to learn more about strain calculations and improve their interpretation of echocardiograms.

The company is already looking ahead to next year, when Upton and his team plan to launch the EchoGo Pro—something they’re promising will be “the first AI system able to predict cardiac disease from echocardiography.”

“We are also planning to expand into other geographic regions, including Europe and Asia,” Upton said. “Our goal is to improve patient outcomes through earlier detection of cardiac disease.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/fda-clears-ai-based-cv-image-analysis-system?utm_source=newsletter&utm_medium=cvb_cardio_imaging

Smartphone app accurately finds, identifies CV implants—and fast

Anicka Slachta | October 07, 2019 | Cardiovascular Imaging

According to the study, the finalized model achieved 95% sensitivity and 98% specificity.

Ferrick et al. said that since their training sample size was somewhat small and limited to a single institution, it would be valuable to validate the model externally. Still, their neural network was able to accurately identify CIEDs on chest radiographs and translate that ability into a phone app.

“Rather than the conventional ‘bench-to-bedside’ approach of translational research, we demonstrated the feasibility of ‘big data-to-bedside’ endeavors,” the team said. “This research has the potential to facilitate device identification in urgent scenarios in medical settings with limited resources.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/smartphone-app-accurately-finds-identifies-cv-implants?utm_source=newsletter&utm_medium=cvb_cardio_imaging

Machine learning cuts cardiac MRI analysis from minutes to seconds

Anicka Slachta | September 24, 2019 | Cardiovascular Imaging

“Cardiovascular MRI offers unparalleled image quality for assessing heart structure and function; however, current manual analysis remains basic and outdated,” Manisty said in a statement. “Automated machine learning techniques offer the potential to change this and radically improve efficiency, and we look forward to further research that could validate its superiority to human analysis.”

It’s estimated that around 150,000 cardiac MRIs are performed in the U.K. each year, she said, and based on that number, her team thinks using AI to read scans could mean saving 54 clinician-days per year at every health center in the country.

“Our dataset of patients with a range of heart diseases who received scans enabled us to demonstrate that the greatest sources of measurement error arise from human factors,” Manisty said. “This indicates that automated techniques are at least as good as humans, with the potential soon to be ‘superhuman’—transforming clinical and research measurement precision.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/machine-learning-speeds-cardiac-mri-analysis?utm_source=newsletter&utm_medium=cvb_cardio_imaging

General SOURCE

From: Cardiovascular Business <news@mail.cardiovascularbusiness.com>

Reply-To: Cardiovascular Business <news@mail.cardiovascularbusiness.com>

Date: Tuesday, December 17, 2019 at 9:31 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Cardiovascular Imaging | December 2019