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

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

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

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

Aviva Lev-Ari
@AVIVA1950

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

Aviva Lev-Ari
@AVIVA1950

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

Aviva Lev-Ari
@AVIVA1950

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

Aviva Lev-Ari
@AVIVA1950

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

Aviva Lev-Ari
@AVIVA1950

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

Aviva Lev-Ari
@AVIVA1950

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

Aviva Lev-Ari
@AVIVA1950

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

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
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Aviva Lev-Ari
@AVIVA1950

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

Coronavirus Portal
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Aviva Lev-Ari
@AVIVA1950

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

Coronavirus Portal
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Aviva Lev-Ari
@AVIVA1950

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

Coronavirus Portal
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Aviva Lev-Ari
@AVIVA1950

#AI

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

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

Aviva Lev-Ari
@AVIVA1950

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

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

Aviva Lev-Ari
@AVIVA1950

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

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

Aviva Lev-Ari
@AVIVA1950

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

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

Aviva Lev-Ari
@AVIVA1950

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

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

Aviva Lev-Ari
@AVIVA1950

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

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

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

Aviva Lev-Ari
@AVIVA1950

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

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

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

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

Aviva Lev-Ari
@AVIVA1950

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

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

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

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

Aviva Lev-Ari
@AVIVA1950

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

Stanford HAI – COVID-19 and AI: A Virtual Conference
COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…
youtube.com
1

Aviva Lev-Ari
@AVIVA1950

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

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

 

Stanford HAI
@StanfordHAI

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

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

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1
8
21

 

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com
2

Aviva Lev-Ari
@AVIVA1950

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

Stanford HAI – COVID-19 and AI: A Virtual Conference
COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…
youtube.com
1

Aviva Lev-Ari
@AVIVA1950

Aviva Lev-Ari
@AVIVA1950

I am at

TODAY

for our Portal @

Coronavirus Portal
CORONAVIRUS PORTAL @LPBI   Launched on 3/14/2020 OPEN TO GUEST AUTHORS on Seven Selected Topics & Lead Curator for Contact:   Development of Medical Counter-measures for 2019-nCoV, Co…
pharmaceuticalintelligence.com
2

Aviva Lev-Ari
@AVIVA1950

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

Stanford HAI – COVID-19 and AI: A Virtual Conference
COVID-19 and AI: A Virtual Conference will address a developing public health crisis. Sponsored by the Stanford Institute for Human-Centered Artificial Intel…
youtube.com
1

Aviva Lev-Ari
@AVIVA1950

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

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

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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
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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.

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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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Artificial Intelligence Innovations in Cardiac Imaging

Reporter: Aviva Lev-Ari, PhD, RN

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

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

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

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

SOURCE

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

 

How to integrate AI into the cardiac imaging pipeline

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

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

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

SOURCE

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

 

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

SOURCE

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

 

FDA clears Ultromics’ AI-based CV image analysis system

Smartphone app accurately finds, identifies CV implants—and fast

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

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

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

SOURCE

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

Machine learning cuts cardiac MRI analysis from minutes to seconds

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

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

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

SOURCE

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

 

General SOURCE

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

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

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

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

Subject: Cardiovascular Imaging | December 2019

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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

 

HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package, the First AI-assisted Cardiac MRI Scan Solution

The future of imaging is here—and FDA cleared.

LOS ALTOS, Calif.–(BUSINESS WIRE)–HeartVista, a pioneer in AI-assisted MRI solutions, today announced that it received 510(k) clearance from the U.S. Food and Drug Administration to deliver its AI-assisted One Click™ MRI acquisition software for cardiac exams. Despite the many advantages of cardiac MRI, or cardiac magnetic resonance (CMR), its use has been largely limited due to a lack of trained technologists, high costs, longer scan time, and complexity of use. With HeartVista’s solution, cardiac MRI is now simple, time-efficient, affordable, and highly consistent.

“HeartVista’s Cardiac Package is a vital tool to enhance the consistency and productivity of cardiac magnetic resonance studies, across all levels of CMR expertise,” said Dr. Raymond Kwong, MPH, Director of Cardiac Magnetic Resonance Imaging at Brigham and Women’s Hospital and Associate Professor of Medicine at Harvard Medical School.

A recent multi-center, outcome-based study (MR-INFORM), published in the New England Journal of Medicine, demonstrated that non-invasive myocardial perfusion cardiovascular MRI was as good as invasive FFR, the previous gold standard method, to guide treatment for patients with stable chest pain, while leading to 20% fewer catheterizations.

“This recent NEJM study further reinforces the clinical literature that cardiac MRI is the gold standard for cardiac diagnosis, even when compared against invasive alternatives,” said Itamar Kandel, CEO of HeartVista. “Our One Click™ solution makes these kinds of cardiac MRI exams practical for widespread adoption. Patients across the country now have access to the only AI-guided cardiac MRI exam, which will deliver continuous imaging via an automated process, minimize errors, and simplify scan operation. Our AI solution generates definitive, accurate and actionable real-time data for cardiologists. We believe it will elevate the standard of care for cardiac imaging, enhance patient experience and access, and improve patient outcomes.”

HeartVista’s FDA-cleared Cardiac Package uses AI-assisted software to prescribe the standard cardiac views with just one click, and in as few as 10 seconds, while the patient breathes freely. A unique artifact detection neural network is incorporated in HeartVista’s protocol to identify when the image quality is below the acceptable threshold, prompting the operator to reacquire the questioned images if desired. Inversion time is optimized with further AI assistance prior to the myocardial delayed-enhancement acquisition. A 4D flow measurement application uses a non-Cartesian, volumetric parallel imaging acquisition to generate high quality images in a fraction of the time. The Cardiac Package also provides preliminary measures of left ventricular function, including ejection fraction, left ventricular volumes, and mass.

HeartVista is presenting its new One Click™ Cardiac Package features at the Radiological Society of North America (RSNA) annual meeting in Chicago, on Dec. 4, 2019, at 2 p.m., in the AI Showcase Theater. HeartVista will also be at Booth #11137 for the duration of the conference, from Dec. 1 through Dec. 5.

About HeartVista

HeartVista believes in leveraging artificial intelligence with the goal of improving access to MRI and improved patient care. The company’s One Click™ software platform enables real-time MRI for a variety of clinical and research applications. Its AI-driven, one-click cardiac localization method received first place honors at the International Society for Magnetic Resonance in Medicine’s Machine Learning Workshop in 2018. The company’s innovative technology originated at the Stanford Magnetic Resonance Systems Research Laboratory. HeartVista is funded by Khosla Ventures, and the National Institute of Health’s Small Business Innovation Research program.

For more information, visit www.heartvista.ai

SOURCE

Reply-To: Kimberly Ha <kimberly.ha@kkhadvisors.com>

Date: Tuesday, October 29, 2019 at 11:01 AM

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

Subject: HeartVista Receives FDA Clearance for First AI-assisted Cardiac MRI Solution

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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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@CHI 1st AI World Conference and Expo, October 23 – October 25, 2019, Seaport World Trade Center, Boston, MA.  Presentations by Four Israeli companies explaining how they use AI technologies in their products @ NEIBC Meetup AI World Conference and Expo, 10/24/2019 @6:30PM Waterfront 1

#AIWORLD

@AIWORLDEXPO

@pharma_BI

@AVIVA1950

Reporter: Aviva Lev-Ari, PhD, RN

 

  • When: October 24, 2019
  • Time: 6:30 pm
  • Where: Seaport World Trade Center, Boston, MA
  • Room Location: Waterfront 1

Speakers Includes:

Registration:

  • To gain access to NEIBC Meetup please RSVP below and use code 1968-EXHP and get complimentary pass to the exhibit
  • If you want to attend the conference, use NEIBC19 discount code and get $200 off conference registration

RSVP NOW

AI World Conference and Expo has become the industry’s largest independent business event focused on the state of the practice of AI in the enterprise. The AI World 3-day program delivers a comprehensive spectrum of content, networking, and business development opportunities, all designed to help you cut through the hype and navigate through the complex landscape of AI business solutions. Attend AI World and learn how innovators are successfully deploying AI and intelligent automation to accelerate innovation efforts, build competitive advantage, drive new business opportunities, and reduce costs.

250+ Speakers

120+ Sponsors

2700+Attendees

100+Sessions

SOURCE

From: “Dan Trajman @ NEIBC” <dan.trajman@neibc.org>

Reply-To: <dan.trajman@neibc.org>

Date: Wednesday, October 23, 2019 at 11:50 AM

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

Subject: Israeli Companies Presenting at AI World October 24, 2019

 

Event Brochure

https://aiworld.com/docs/librariesprovider28/agenda/19/aiworld-conference-expo-2019.pdf

 

Plenary Program

WEDNESDAY, OCTOBER 23

9:00 AM SUMMIT KICK OFF: AI Becomes Real

Scott Lundstrom, Group Vice President and General Manager, IDC

Government and Health Insights, IDC and AI World, Conference Co-Chair

 

9:10 AM SUMMIT KEYNOTE: Business Strategy with Artificial Intelligence

Sam Ransbotham, PhD, Professor, Academic Contributing Editor,

Information Systems, Boston College; MIT Sloan Management Review

 

9:35 AM EXECUTIVE ROUNDTABLE:

AI Drives Innovation in Enterprise Applications

Moderator: Mickey North-Rizza, Research Vice President, Enterprise Applications, IDC

Panelists:

David Castillo, PhD, Managing Vice President, Machine Learning, Capital One

Mukesh Dalal, PhD, Chief Analytics Officer & Chief Data Scientist, Bose Corporation

Madhumita Bhattacharyya, Managing Director – Enterprise Data & Analytics,

Protiviti Sasha Caskey, CTO & Co-Founder, Kasisto

 

10:05 AM KEYNOTE: Evolving Role of CDAOS in the New Era – An Organizational Construct

Anju Gupta, Vice President, Chief Data and Analytics Officer, Enterprise Holdings

 

10:30 – 10:50 AM NETWORKING BREAK

 

10:50 AM EXECUTIVE ROUNDTABLE:

 

The Evolution of Conversational Assistants

 

Moderator:

Reenita Malholtra Hora, Director of Marketing & Communications, SRI International

Panelists:

William Mark, PhD, President, SRI

Karen Myers, Lab Director, SRI International’s AI Center

 

11:20 AM Talk Title to be Announced

Genevy Dimitrion VP, Enterprise Data and Analytics, Humana

 

11:40 AM How AI Maturity Impacts a Winning Corporate Strategy

Ritu Jyoti, Program Vice President, IDC

 

4:20 PM PLENARY KEYNOTE PANEL:

Learning from XPRIZE Startups to Achieve Successful AI Innovation

Moderator:

Devin Krotman, Director, IBM Watson

AI XPRIZE,

XPRIZE Foundation

 

Panelists: Eleni Miltsakaki, Founder and CEO, Choosito

Ellie Gordon, Founder, CEO, & Designer, Behaivior AI

Daniel Fortin, President, AITera Inc.

 

12:00 PM LUNCHEON KEYNOTE:

Case Studies of Conversational AI – Real Deployments at Scale

Jim Freeze, Chief Marketing Officer, Interactions

 

Sponsored by Ben Bauks, Senior Business Systems Analyst, Constant Contact

 

THURSDAY, OCTOBER 24

 

8:20 AM BREAKFAST KEYNOTE:

The Promise and Pain of Computer Vision in Retail, Healthcare, and Agriculture

Ben Schneider, Vice President, Product, Alegion

 

9:00 AM CONFERENCE INTRODUCTION

Eliot Weinman, Founder & Conference Chair, AI World; Executive Editor, AI Trends

 

9:05 AM INTRODUCTORY REMARKS

Scott Lundstrom, Group Vice President and General Manager, IDC Government and

Health Insights, IDC and AI World, Conference Co-Chair

 

9:15 AM KEYNOTE PRESENTATION:

The Human Strategy

Alex Sandy Pentland, PhD, Professor, Engineering, Business, Media Lab, MIT

 

9:45 AM KEYNOTE:

Uber’s Intelligent Insights Assistant

Franziska Bell, PhD, Director, Data Science, Data Science Platforms, Uber

 

10:15 AM KEYNOTE:

AI in Finance: Present and Future, Hype and Reality

Charles Elkan, PhD, Managing Director, Goldman Sachs

 

10:40 – 11:00 AM COFFEE BREAK

 

11:00 AM KEYNOTE:

AI at Work in Legal, News and Tax & Accounting

Khalid Al-Kofahi, PhD, Vice President, Research and Development, Head –

Center for AI and Cognitive Computing, Thomson Reuters

 

11:25AM EXECUTIVE ROUNDTABLE:

Disinformation, Infosec, Cognitive Security and Influence Manipulation

Moderator:

Michael Krigsman, Industry Analyst, CXOTalk

 

Panelist:

Sara-Jayne Terp, Data Scientist, Bodacea Light Industries LLC

Bob Gourley, Co-Founder and CTO, OODA LLC

Pablo Breuer, Director of US Special Operations Command Donovan Group and Senior Military Advisor and Innovation Officer, SOFWERX

Anthony Scriffignano, PhD, SVP, Chief Data Scientist, Dun & Bradstreet

 

Sponsored by

PUSHING THE BOUNDARIES OF AI – Providing the expertise required to accelerate the evolution of human progress in the age of artificial intelligence http://dellemc.com/AI

 

11:30 AM KEYNOTE:

How AI is Helping to Improve Canadian Lives Through AML

Vishal Gossain, Vice President, Global Risk Management, ScotiaBank

 

FRIDAY, OCTOBER 25

 

8:15 AM KEYNOTE:

AI World Society Roundtable on AI-Healthcare

Moderator:

Ed Burns, Site Editor, TechTarget

 

Panelists:

Professor David Silbersweig, Board Member of BGF, Harvard Medical

School

Professor Mai Trong Khoa, PhD, Chairman of the Nuclear Medicine and Oncology

Council, Director of the Gene-Stem cell Center, Bach Mai hospital, Senior lecturer,

Hanoi Medical University, Secrectary of the National Council of Professorship in

Medicine in Vietnam

Truong Van Phuoc, PhD, Former Acting Chairman, State Inspectory Committee

of Finance of Vietnam, Senior Advisor to Chairman, Vietbank

Truong Vinh Long, MD, CEO, Gia An 115 Hospital

 

10:00 AM KEYNOTE:

AI in Pharma: Where we are Today and How we Will Succeed in the Future

Natalija Jovanovic, PhD, Chief Digital Officer, Sanofi Pasteur

 

10:30 AM Startup Awards Announcement

John Desmond, Principal at JD Content Services, Editor AI Trends

 

10:35 – 10:50 AM COFFEE BREAK IN THE EXPO

 

10:50 AM EXECUTIVE ROUNDTABLE:

Enterprise AI Innovations

Moderator:

Nick Patience, Founder & Research Vice President, Software, 451 Research

Rudina Seseri, Founder and Managing Partner, Glasswing Venture

Norbert Monfort, Vice President, IT Transformation and Innovation, Assurant Global Technology

Dawn Fitzgerald, Director of Digital Transformation Data Center Operations, Schneider Electric

 

PLENARY PROGRAM

 

8:45 AM CONFERENCE INTRODUCTION

Scott Lundstrom, Group Vice President and General Manager, IDC Government and

Health Insights, IDC and AI World, Conference Co-Chair

 

8:50 AM KEYNOTE:

Artificial Intelligence in Sustainable Development: An Educational Perspective

Enver Yucel, Chairman, Bahçeşehir University

 

9:00 AM KEYNOTE:

Enhancing Human Capability with Intelligent Machine Teammates

Julie Shah, Associate Professor, Dept of Aeronautics and Astronautics, Computer Science and AI Lab, MIT

 

9:30 AM KEYNOTE:

Democracy, Ethics and the Rule of Law in the Age of Artificial Intelligence

Paul F. Nemitz, Principal Advisor in the Directorate-General for Justice and Consumers of the European Commission

 

12:00 PM LUNCHEON KEYNOTE:

How AI/ML is Changing the Face of Enterprise IT

Robert Ames, Senior Director, National Technology Strategy,

VMware Research, VMware

 

SOURCE

https://aiworld.com/docs/librariesprovider28/agenda/19/aiworld-conference-expo-2019.pdf

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