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Archive for the ‘Artificial Intelligence in CANCER’ Category


Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

Register for FREE at https://www.aacr.org/

uesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Bioinformatics and Systems Biology

The Clinical Proteomic Tumor Analysis Consortium: Resources and Data Dissemination

This session will provide information regarding methodologic and computational aspects of proteogenomic analysis of tumor samples, particularly in the context of clinical trials. Availability of comprehensive proteomic and matching genomic data for tumor samples characterized by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) program will be described, including data access procedures and informatic tools under development. Recent advances on mass spectrometry-based targeted assays for inclusion in clinical trials will also be discussed.

Amanda G Paulovich, Shankha Satpathy, Meenakshi Anurag, Bing Zhang, Steven A Carr

Methods and tools for comprehensive proteogenomic characterization of bulk tumor to needle core biopsies

Shankha Satpathy
  • TCGA has 11,000 cancers with >20,000 somatic alterations but only 128 proteins as proteomics was still young field
  • CPTAC is NCI proteomic effort
  • Chemical labeling approach now method of choice for quantitative proteomics
  • Looked at ovarian and breast cancers: to measure PTM like phosphorylated the sample preparation is critical

 

Data access and informatics tools for proteogenomics analysis

Bing Zhang
  • Raw and processed data (raw MS data) with linked clinical data can be extracted in CPTAC
  • Python scripts are available for bioinformatic programming

 

Pathways to clinical translation of mass spectrometry-based assays

Meenakshi Anurag

·         Using kinase inhibitor pulldown (KIP) assay to identify unique kinome profiles

·         Found single strand break repair defects in endometrial luminal cases, especially with immune checkpoint prognostic tumors

·         Paper: JNCI 2019 analyzed 20,000 genes correlated with ET resistant in luminal B cases (selected for a list of 30 genes)

·         Validated in METABRIC dataset

·         KIP assay uses magnetic beads to pull out kinases to determine druggable kinases

·         Looked in xenografts and was able to pull out differential kinomes

·         Matched with PDX data so good clinical correlation

·         Were able to detect ESR1 fusion correlated with ER+ tumors

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Survivorship

Artificial Intelligence and Machine Learning from Research to the Cancer Clinic

The adoption of omic technologies in the cancer clinic is giving rise to an increasing number of large-scale high-dimensional datasets recording multiple aspects of the disease. This creates the need for frameworks for translatable discovery and learning from such data. Like artificial intelligence (AI) and machine learning (ML) for the cancer lab, methods for the clinic need to (i) compare and integrate different data types; (ii) scale with data sizes; (iii) prove interpretable in terms of the known biology and batch effects underlying the data; and (iv) predict previously unknown experimentally verifiable mechanisms. Methods for the clinic, beyond the lab, also need to (v) produce accurate actionable recommendations; (vi) prove relevant to patient populations based upon small cohorts; and (vii) be validated in clinical trials. In this educational session we will present recent studies that demonstrate AI and ML translated to the cancer clinic, from prognosis and diagnosis to therapy.
NOTE: Dr. Fish’s talk is not eligible for CME credit to permit the free flow of information of the commercial interest employee participating.

Ron C. Anafi, Rick L. Stevens, Orly Alter, Guy Fish

Overview of AI approaches in cancer research and patient care

Rick L. Stevens
  • Deep learning is less likely to saturate as data increases
  • Deep learning attempts to learn multiple layers of information
  • The ultimate goal is prediction but this will be the greatest challenge for ML
  • ML models can integrate data validation and cross database validation
  • What limits the performance of cross validation is the internal noise of data (reproducibility)
  • Learning curves: not the more data but more reproducible data is important
  • Neural networks can outperform classical methods
  • Important to measure validation accuracy in training set. Class weighting can assist in development of data set for training set especially for unbalanced data sets

Discovering genome-scale predictors of survival and response to treatment with multi-tensor decompositions

Orly Alter
  • Finding patterns using SVD component analysis. Gene and SVD patterns match 1:1
  • Comparative spectral decompositions can be used for global datasets
  • Validation of CNV data using this strategy
  • Found Ras, Shh and Notch pathways with altered CNV in glioblastoma which correlated with prognosis
  • These predictors was significantly better than independent prognostic indicator like age of diagnosis

 

Identifying targets for cancer chronotherapy with unsupervised machine learning

Ron C. Anafi
  • Many clinicians have noticed that some patients do better when chemo is given at certain times of the day and felt there may be a circadian rhythm or chronotherapeutic effect with respect to side effects or with outcomes
  • ML used to determine if there is indeed this chronotherapy effect or can we use unstructured data to determine molecular rhythms?
  • Found a circadian transcription in human lung
  • Most dataset in cancer from one clinical trial so there might need to be more trials conducted to take into consideration circadian rhythms

Stratifying patients by live-cell biomarkers with random-forest decision trees

Stratifying patients by live-cell biomarkers with random-forest decision trees

Guy Fish CEO Cellanyx Diagnostics

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Molecular and Cellular Biology/Genetics, Bioinformatics and Systems Biology, Prevention Research

The Wound Healing that Never Heals: The Tumor Microenvironment (TME) in Cancer Progression

This educational session focuses on the chronic wound healing, fibrosis, and cancer “triad.” It emphasizes the similarities and differences seen in these conditions and attempts to clarify why sustained fibrosis commonly supports tumorigenesis. Importance will be placed on cancer-associated fibroblasts (CAFs), vascularity, extracellular matrix (ECM), and chronic conditions like aging. Dr. Dvorak will provide an historical insight into the triad field focusing on the importance of vascular permeability. Dr. Stewart will explain how chronic inflammatory conditions, such as the aging tumor microenvironment (TME), drive cancer progression. The session will close with a review by Dr. Cukierman of the roles that CAFs and self-produced ECMs play in enabling the signaling reciprocity observed between fibrosis and cancer in solid epithelial cancers, such as pancreatic ductal adenocarcinoma.

Harold F Dvorak, Sheila A Stewart, Edna Cukierman

 

The importance of vascular permeability in tumor stroma generation and wound healing

Harold F Dvorak

Aging in the driver’s seat: Tumor progression and beyond

Sheila A Stewart

Why won’t CAFs stay normal?

Edna Cukierman

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

 

 

 

 

 

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

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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/

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Collaborative innovation has never been more important.

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

Join top leaders guiding the response, technology and people confronting this century’s greatest health challenge.

Priya Abani

CEO, AliveCor

General Keith Alexander

Co-CEO, IronNet; Former NSA Head

Stéphane Bancel

CEO, Moderna

Marc Casper

CEO, Thermo Fisher

Timothy Ferris, MD

CEO, MGPO; Professor, HMS

John Fernandez  

President, MEE; President, Ambulatory Care, Mass General Brigham

 

John Fish

CEO, Suffolk; BH Board Chair

JF Formela, MD

Partner, Atlas Venture

Jan Garfinkle

Manager Partner, Arboretum Ventures; Chair, NVCA

Phillip Gross

Managing Director, Adage Capital Management

Julia Hu

CEO, Lark Health

Anjali Kataria

CEO, Mytonomy

Roger Kitterman

VP, Managing Partner, Mass General Brigahm Fund

Jonathan Kraft

President, Kraft Group; Chair, MGH Board

Brooke LeVasseur

CEO, AristaMD

Mike Mahoney

CEO, Boston Scientific

Bernd Montag, PhD

CEO, Siemens Healthineers

Kieran Murphy

CEO, GE Healthcare

Elizabeth Nabel, MD

President, BH; Professor, HMS

Matt Sause

CEO, Roche Diagnostics

Peter Slavin, MD

President, MGH; Professor, HMS

Scott Sperling

Co-President, TH Lee; Chair, Mass General Brigham Board

Christopher Viehbacher

Managing Partner, Gurnet Point Capital

Michel Vounatsos

CEO, Biogen

Collaborative Innovation

Together we meet the challenge of the coronavirus and share our commitment to the future of medicine.

 

Anne Klibanski, MD

CEO, Mass General Brigham

Amy Abernethy, MD, PhD

Principal Deputy Commissioner and Acting CIO, FDA

PANEL

FDA Role in Managing Crisis and Anticipating the Next

Elizabeth Nabel, MD

President, Brigham Health; Professor of Medicine, HMS

PANEL

Care in the Next 18 Months 

Karen DeSalvo, MD

Chief Health Officer, Google Health

PANEL

Role of AI and Big Data in Fighting COVID-19 

Dawn Sugarman, PhD

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

PANEL

Digital Therapeutics

Ann Prestipino

SVP; Incident Commander, MGH; Teaching Associate, HMS

PANEL

Real Time: Front Line Innovation

Hadine Joffe, MD

Vice Chair, Research, Psychiatry; Executive Director, Mary Horrigan Connors Center for Women’s Health and Gender Biology, BH; Paula Johnson Professor, Women’s Health, HMS

PANEL

Digital Therapeutics

Priya Abani

CEO, AliveCor

PANEL

Digital Therapeutics

Julia Hu

CEO, Lark Health

PANEL

Digital Therapeutics

Jan Garfinkle

Manager Partner, Arboretum Ventures; Chair NVCA

PANEL

Early Stage Investment Environment

Anjali Kataria

CEO, Mytonomy

PANEL

Patient Experience During the Pandemic

Brooke LeVasseur

CEO, AristaMD

PANEL

Digital Health Becomes a Pillar

Julie Lankiewicz

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

PANEL

Emergency and Urgent Care

 

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/

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2020 World Medical Innovation Forum – COVID-19, AI  – Life Science and Digital Health Investments, MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET

 

 

 

Life science and digital health investments have continued at a strong pace during the COVID-19 crisis. Senior investment leaders discuss what to expect. Will:

  • social distancing affect deal making?
  • key asset categories remain strong – venture, private equity, public offerings, acquisitions?
  • valuations hold up in some categories while others fall?

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


Jan Garfinkle
, Founder & Manager Partner, Arboretum Ventures, Chair NVCA

Phillip Gross, Managing Director, Adage Capital Management

Christopher Viehbacher, Managing Partner, Gurnet Point Capital

 

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/

Read Full Post »


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

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

Add Panel to Calendar

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

Add Panel to Calendar

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, MDChief, 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

Add Panel to Calendar

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

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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

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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

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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

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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 AndersonSVP, 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

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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

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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

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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

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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

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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

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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

 

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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

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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

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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

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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

 

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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

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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

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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

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5:05 – 5:10 PM
Closing Remarks
Gregg Meyer, MDChief 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

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.

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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

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

Gut Journal
@Gut_BMJ
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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
Follow
A new open access clinical journal, published by 

, influencing clinical practice and strengthening health systems

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Eric Topol
@EricTopol
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.

Image

 

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

 

 

 

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

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 RadiologyRadiologyInsights 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.
  1. “Artificial Intelligence and Precision Education: How AI Can Revolutionize Training in Radiology” | Monday, Dec. 2 | 8:30 – 10 a.m. | Room: E450A
  2. “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
  3. “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

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 Sourcehttps://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

 

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 3rd 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

 

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

Karin Dembrower Yue LiuHossein AzizpourMartin EklundKevin SmithPeter LindholmFredrik Strand

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 cm2P < .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

Radiology2019

Volume: 0Issue: 0

Radiology2019

Volume: 293Issue: 2pp. 246-259

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

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

SOURCE

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

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Google AI improves accuracy of reading mammograms, study finds

Google AI improves accuracy of reading mammograms, study finds

Google CFO Ruth Porat has blogged about twice battling breast cancer.

Artificial intelligence was often more accurate than radiologists in detecting breast cancer from mammograms in a study conducted by researchers using Google AI technology.

The study, published in the journal Nature, used mammograms from approximately 90,000 women in which the outcomes were known to train technology from Alphabet Inc’s DeepMind AI unit, now part of Google Health, Yahoo news reported.

The AI system was then used to analyze images from 28,000 other women and often diagnosed early cancers more accurately than the radiologists who originally interpreted the mammograms.

In another test, AI outperformed six radiologists in reading 500 mammograms. However, while the AI system found cancers the humans missed, it also failed to find cancers flagged by all six radiologists, reports The New York Times.

The researchers said the study “paves the way” for further clinical trials.

Writing in NatureEtta D. Pisano, chief research officer at the American College of Radiology and professor in residence at Harvard Medical School, noted, “The real world is more complicated and potentially more diverse than the type of controlled research environment reported in this study.”

Ruth Porat, senior vice president and chief financial officer Alphabet, Inc., wrote in a company blog titled “Breast cancer and tech…a reason for optimism” in October about twice battling the disease herself, and the importance of her company’s application of AI to healthcare innovations.

She said that focus had already led to the development of a deep learning algorithm to help pathologists assess tissue associated with metastatic breast cancer.

“By pinpointing the location of the cancer more accurately, quickly and at a lower cost, care providers might be able to deliver better treatment for more patients,” she wrote.

Google also has created algorithms that help medical professionals diagnose lung cancer, and eye disease in people with diabetes, per the Times.

Porat acknowledged that Google’s research showed the best results occur when medical professionals and technology work together.

Any insights provided by AI must be “paired with human intelligence and placed in the hands of skilled researchers, surgeons, oncologists, radiologists and others,” she said.

Anne Stych is a staff writer for Bizwomen.
Industries:

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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

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Yu Fu1, Alexander W Jung1, Ramon Viñas Torne1, Santiago Gonzalez1,2, Harald Vöhringer1, Mercedes Jimenez-Linan3, Luiza Moore3,4, and Moritz Gerstung#1,5 # to whom correspondence should be addressed 1) European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK. 2) Current affiliation: Institute for Research in Biomedicine (IRB Barcelona), Parc Científic de Barcelona, Barcelona, Spain. 3) Department of Pathology, Addenbrooke’s Hospital, Cambridge, UK. 4) Wellcome Sanger Institute, Hinxton, UK 5) European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.

Correspondence:

Dr Moritz Gerstung European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton, CB10 1SA UK. Tel: +44 (0) 1223 494636 E-mail: moritz.gerstung@ebi.ac.uk

Abstract

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Here we use deep transfer learning to quantify histopathological patterns across 17,396 H&E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 25/27 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.

SOURCE

https://www.biorxiv.org/content/10.1101/813543v1

Key points

● Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types

● Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations

● Wide-spread correlations with gene expression indicative of immune infiltration and proliferation

● Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers

 

Discussion

Here we presented PC-CHiP, a pan-cancer transfer learning approach to extract computational histopathological features across 42 cancer and normal tissue types and their genomic, molecular and prognostic associations. Histopathological features, originally derived to classify different tissues, contained rich histologic and morphological signals predictive of a range of genomic and transcriptomic changes as well as survival. This shows that computer vision not only has the capacity to highly accurately reproduce predefined tissue labels, but also that this quantifies diverse histological patterns, which are predictive of a broad range of genomic and molecular traits, which were not part of the original training task. As the predictions are exclusively based on standard H&E-stained tissue sections, our analysis highlights the high potential of computational histopathology to digitally augment existing histopathological workflows. The strongest genomic associations were found for whole genome duplications, which can in part be explained by nuclear enlargement and increased nuclear intensities, but seemingly also stems from tumour grade and other histomorphological patterns contained in the high-dimensional computational histopathological features. Further, we observed associations with a range of chromosomal gains and losses, focal deletions and amplifications as well as driver gene mutations across a number of cancer types. These data demonstrate that genomic alterations change the morphology of cancer cells, as in the case of WGD, but possibly also that certain aberrations preferentially occur in distinct cell types, reflected by the tumor histology. Whatever is the cause or consequence in this equation, these associations lay out a route towards genomically defined histopathology subtypes, which will enhance and refine conventional assessment. Further, a broad range of transcriptomic correlations was observed reflecting both immune cell infiltration and cell proliferation that leads to higher tumor densities. These examples illustrated the remarkable property that machine learning does not only establish novel molecular associations from pre-computed histopathological feature sets but also allows the localisation of these traits within a larger image. While this exemplifies the power of a large scale data analysis to detect and localise recurrent patterns, it is probably not superior to spatially annotated training data. Yet such data can, by definition, only be generated for associations which are known beforehand. This appears straightforward, albeit laborious, for existing histopathology classifications, but more challenging for molecular readouts. Yet novel spatial transcriptomic44,45 and sequencing technologies46 bring within reach spatially matched molecular and histopathological data, which would serve as a gold standard in combining imaging and molecular patterns. Across cancer types, computational histopathological features showed a good level of prognostic relevance, substantially improving prognostic accuracy over conventional grading and histopathological subtyping in the majority of cancers. It is this very remarkable that such predictive It is made available under a CC-BY-NC 4.0 International license. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. bioRxiv preprint first posted online Oct. 25, 2019; doi: http://dx.doi.org/10.1101/813543. The copyright holder for this preprint signals can be learned in a fully automated fashion. Still, at least at the current resolution, the improvement over a full molecular and clinical workup was relatively small. This might be a consequence of the far-ranging relations between histopathology and molecular phenotypes described here, implying that histopathology is a reflection of the underlying molecular alterations rather than an independent trait. Yet it probably also highlights the challenges of unambiguously quantifying histopathological signals in – and combining signals from – individual areas, which requires very large training datasets for each tumour entity. From a methodological point of view, the prediction of molecular traits can clearly be improved. In this analysis, we adopted – for the reason of simplicity and to avoid overfitting – a transfer learning approach in which an existing deep convolutional neural network, developed for classification of everyday objects, was fine tuned to predict cancer and normal tissue types. The implicit imaging feature representation was then used to predict molecular traits and outcomes. Instead of employing this two-step procedure, which risks missing patterns irrelevant for the initial classification task, one might directly employ either training on the molecular trait of interest, or ideally multi-objective learning. Further improvement may also be related to the choice of the CNN architecture. Everyday images have no defined scale due to a variable z-dimension; therefore, the algorithms need to be able to detect the same object at different sizes. This clearly is not the case for histopathology slides, in which one pixel corresponds to a defined physical size at a given magnification. Therefore, possibly less complex CNN architectures may be sufficient for quantitative histopathology analyses, and also show better generalisation. Here, in our proof-of-concept analysis, we observed a considerable dependence of the feature representation on known and possibly unknown properties of our training data, including the image compression algorithm and its parameters. Some of these issues could be overcome by amending and retraining the network to isolate the effect of confounding factors and additional data augmentation. Still, given the flexibility of deep learning algorithms and the associated risk of overfitting, one should generally be cautious about the generalisation properties and critically assess whether a new image is appropriately represented. Looking forward, our analyses revealed the enormous potential of using computer vision alongside molecular profiling. While the eye of a trained human may still constitute the gold standard for recognising clinically relevant histopathological patterns, computers have the capacity to augment this process by sifting through millions of images to retrieve similar patterns and establish associations with known and novel traits. As our analysis showed this helps to detect histopathology patterns associated with a range of genomic alterations, transcriptional signatures and prognosis – and highlight areas indicative of these traits on each given slide. It is therefore not too difficult to foresee how this may be utilised in a computationally augmented histopathology workflow enabling more precise and faster diagnosis and prognosis. Further, the ability to quantify a rich set of histopathology patterns lays out a path to define integrated histopathology and molecular cancer subtypes, as recently demonstrated for colorectal cancers47 .

Lastly, our analyses provide It is made available under a CC-BY-NC 4.0 International license. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

bioRxiv preprint first posted online Oct. 25, 2019; doi: http://dx.doi.org/10.1101/813543.

The copyright holder for this preprint proof-of-concept for these principles and we expect them to be greatly refined in the future based on larger training corpora and further algorithmic refinements.

SOURCE

https://www.biorxiv.org/content/biorxiv/early/2019/10/25/813543.full.pdf

 

Other related articles published in this Open Access Online Scientific Journal include the following: 

 

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/

 

631 articles had in their Title the keyword “Pathology”

https://pharmaceuticalintelligence.com/?s=Pathology

 

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

Reporter and Curator: Dror Nir, PhD

 

The debate on the function AI could or should realize in modern radiology is buoyant presenting wide spectrum of positive expectations and also fears.

The article: A Deep Learning Model to Triage Screening Mammograms: A Simulation Study that was published this month shows the best, and very much feasible, utility for AI in radiology at the present time. It would be of great benefit for radiologists and patients if such applications will be incorporated (with all safety precautions taken) into routine practice as soon as possible.

In a simulation study, a deep learning model to triage mammograms as cancer free improves workflow efficiency and significantly improves specificity while maintaining a noninferior sensitivity.

Background

Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency.

Purpose

To develop a DL model to triage a portion of mammograms as cancer free, improving performance and workflow efficiency.

Materials and Methods

In this retrospective study, 223 109 consecutive screening mammograms performed in 66 661 women from January 2009 to December 2016 were collected with cancer outcomes obtained through linkage to a regional tumor registry. This cohort was split by patient into 212 272, 25 999, and 26 540 mammograms from 56 831, 7021, and 7176 patients for training, validation, and testing, respectively. A DL model was developed to triage mammograms as cancer free and evaluated on the test set. A DL-triage workflow was simulated in which radiologists skipped mammograms triaged as cancer free (interpreting them as negative for cancer) and read mammograms not triaged as cancer free by using the original interpreting radiologists’ assessments. Sensitivities, specificities, and percentage of mammograms read were calculated, with and without the DL-triage–simulated workflow. Statistics were computed across 5000 bootstrap samples to assess confidence intervals (CIs). Specificities were compared by using a two-tailed t test (P < .05) and sensitivities were compared by using a one-sided t test with a noninferiority margin of 5% (P < .05).

Results

The test set included 7176 women (mean age, 57.8 years ± 10.9 [standard deviation]). When reading all mammograms, radiologists obtained a sensitivity and specificity of 90.6% (173 of 191; 95% CI: 86.6%, 94.7%) and 93.5% (24 625 of 26 349; 95% CI: 93.3%, 93.9%). In the DL-simulated workflow, the radiologists obtained a sensitivity and specificity of 90.1% (172 of 191; 95% CI: 86.0%, 94.3%) and 94.2% (24 814 of 26 349; 95% CI: 94.0%, 94.6%) while reading 80.7% (21 420 of 26 540) of the mammograms. The simulated workflow improved specificity (P = .002) and obtained a noninferior sensitivity with a margin of 5% (P < .001).

Conclusion

This deep learning model has the potential to reduce radiologist workload and significantly improve specificity without harming sensitivity.

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Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

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

 

Pancreatic cancer is a significant cause of cancer mortality; therefore, the development of early diagnostic strategies and effective treatment is essential. Improvements in imaging technology, as well as use of biomarkers are changing the way that pancreas cancer is diagnosed and staged. Although progress in treatment for pancreas cancer has been incremental, development of combination therapies involving both chemotherapeutic and biologic agents is ongoing.

 

Cancer is an evolutionary disease, containing the hallmarks of an asexually reproducing unicellular organism subject to evolutionary paradigms. Pancreatic ductal adenocarcinoma (PDAC) is a particularly robust example of this phenomenon. Genomic features indicate that pancreatic cancer cells are selected for fitness advantages when encountering the geographic and resource-depleted constraints of the microenvironment. Phenotypic adaptations to these pressures help disseminated cells to survive in secondary sites, a major clinical problem for patients with this disease.

 

The immune system varies in cell types, states, and locations. The complex networks, interactions, and responses of immune cells produce diverse cellular ecosystems composed of multiple cell types, accompanied by genetic diversity in antigen receptors. Within this ecosystem, innate and adaptive immune cells maintain and protect tissue function, integrity, and homeostasis upon changes in functional demands and diverse insults. Characterizing this inherent complexity requires studies at single-cell resolution. Recent advances such as massively parallel single-cell RNA sequencing and sophisticated computational methods are catalyzing a revolution in our understanding of immunology.

 

PDAC is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. In the present study to comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, single-cell RNA-seq (scRNA-seq) was employed to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases. The diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, were identified in PDAC.

 

The researchers found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, it was found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, the findings provided a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/31273297

 

https://www.ncbi.nlm.nih.gov/pubmed/21491194

 

https://www.ncbi.nlm.nih.gov/pubmed/27444064

 

https://www.ncbi.nlm.nih.gov/pubmed/28983043

 

https://www.ncbi.nlm.nih.gov/pubmed/24976721

 

https://www.ncbi.nlm.nih.gov/pubmed/27693023

 

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