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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
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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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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

Read Full Post »


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

Read Full Post »


Artificial Intelligence and Cardiovascular Disease

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

 

Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.

 

The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.

 

Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.

 

In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.

 

One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.

 

Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.

 

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.

 

The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.

 

Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.

 

References:

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.bhf.org.uk/informationsupport/heart-matters-magazine/research/artificial-intelligence

 

https://www.medicaldevice-network.com/news/heart-attack-artificial-intelligence/

 

https://www.nature.com/articles/s41569-019-0158-5

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711980/

 

www.j-pcs.org/article.asp

http://www.onlinejacc.org/content/71/23/2668

http://www.scielo.br/pdf/ijcs/v30n3/2359-4802-ijcs-30-03-0187.pdf

 

https://www.escardio.org/The-ESC/Press-Office/Press-releases/How-artificial-intelligence-is-tackling-heart-disease-Find-out-at-ICNC-2019

 

https://clinicaltrials.gov/ct2/show/NCT03877614

 

https://www.europeanpharmaceuticalreview.com/news/82870/artificial-intelligence-ai-heart-disease/

 

https://www.frontiersin.org/research-topics/10067/current-and-future-role-of-artificial-intelligence-in-cardiac-imaging

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.sciencedaily.com/releases/2019/05/190513104505.htm

 

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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

Best three machine-learning methods with the best predictive capacity had area under the ROC curve (AUC) scores of

  • 0.7086 (quadratic discriminant analysis),
  • 0.7084 (NaiveBayes) and
  • 0.7042 (neural networks)
  • the conventional risk-scaling methods—which are widely used in clinical practice in Spain—fell in at 11th and 12th places, with AUCs below 0.64.

 

Machine learning to predict cardiovascular risk

First published: 01 July 2019

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:10.1111/ijcp.13389

Abstract

Aims

To analyze the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales.

Methods

We calculated cardiovascular risk by means of 15 machine‐learning methods and using the SCORE and REGICOR scales and in 38,527 patients in the Spanish ESCARVAL RISK cohort, with five‐year follow‐up. We considered patients to be at high risk when the risk of a cardiovascular event was over 5% (according to SCORE and machine learning methods) or over 10% (using REGICOR). The area under the receiver operating curve (AUC) and the C‐index were calculated, as well as the diagnostic accuracy rate, error rate, sensitivity, specificity, positive and negative predictive values, positive likelihood ratio, and number of needed to treat to prevent a harmful outcome.

Results

The method with the greatest predictive capacity was quadratic discriminant analysis, with an AUC of 0.7086, followed by NaiveBayes and neural networks, with AUCs of 0.7084 and 0.7042, respectively. REGICOR and SCORE ranked 11th and 12th, respectively, in predictive capacity, with AUCs of 0.63. Seven machine learning methods showed a 7% higher predictive capacity (AUC) as well as higher sensitivity and specificity than the REGICOR and SCORE scales.

Conclusions

Ten of the 15 machine learning methods tested have a better predictive capacity for cardiovascular events and better classification indicators than the SCORE and REGICOR risk assessment scales commonly used in clinical practice in Spain. Machine learning methods should be considered in the development of future cardiovascular risk scales.

This article is protected by copyright. All rights reserved.

SOURCE

https://onlinelibrary.wiley.com/doi/abs/10.1111/ijcp.13389

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Real Time Coverage @BIOConvention #BIO2019: Machine Learning and Artificial Intelligence: Realizing Precision Medicine One Patient at a Time

Reporter: Stephen J Williams, PhD @StephenJWillia2

The impact of Machine Learning (ML) and Artificial Intelligence (AI) during the last decade has been tremendous. With the rise of infobesity, ML/AI is evolving to an essential capability to help mine the sheer volume of patient genomics, omics, sensor/wearables and real-world data, and unravel the knot of healthcare’s most complex questions.

Despite the advancements in technology, organizations struggle to prioritize and implement ML/AI to achieve the anticipated value, whilst managing the disruption that comes with it. In this session, panelists will discuss ML/AI implementation and adoption strategies that work. Panelists will draw upon their experiences as they share their success stories, discuss how to implement digital diagnostics, track disease progression and treatment, and increase commercial value and ROI compared against traditional approaches.

  • most of trials which are done are still in training AI/ML algorithms with training data sets.  The best results however have been about 80% accuracy in training sets.  Needs to improve
  • All data sets can be biased.  For example a professor was looking at heartrate using a IR detector on a wearable but it wound up that different types of skin would generate a different signal to the detector so training sets maybe population biases (you are getting data from one group)
  • clinical grade equipment actually haven’t been trained on a large set like commercial versions of wearables, Commercial grade is tested on a larger study population.  This can affect the AI/ML algorithms.
  • Regulations:  The regulatory bodies responsible is up to debate.  Whether FDA or FTC is responsible for AI/ML in healtcare and healthcare tech and IT is not fully decided yet.  We don’t have the guidances for these new technologies
  • some rules: never use your own encryption always use industry standards especially when getting personal data from wearables.  One hospital corrupted their system because their computer system was not up to date and could not protect against a virus transmitted by a wearable.
  • pharma companies understand they need to increase value of their products so very interested in how AI/ML can be used.

Please follow LIVE on TWITTER using the following @ handles and # hashtags:

@Handles

@pharma_BI

@AVIVA1950

@BIOConvention

# Hashtags

#BIO2019 (official meeting hashtag)

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Applying AI to Improve Interpretation of Medical Imaging

Author and Curator: Dror Nir, PhD

 

 

images

The idea that we can use machines’ intelligence to help us perform daily tasks is not an alien any more. As consequence, applying AI to improve the assessment of patients’ clinical condition is booming. What used to be the field of daring start-ups became now a playground for the tech-giants; Google, Amazon, Microsoft and IBM.

Interpretation of medical-Imaging involves standardised workflows and requires analysis of many data-items. Also, it is well established that human-subjectivity is a barrier to reproducibility and transferability of medical imaging results (evident by the reports on high intraoperative variability in  imaging-interpretation).Accepting the fact that computers are better suited that humans to perform routine, repeated tasks involving “big-data” analysis makes AI a very good candidate to improve on this situation.Google’s vision in that respect: “Machine learning has dozens of possible application areas, but healthcare stands out as a remarkable opportunity to benefit people — and working closely with clinicians and medical providers, we’re developing tools that we hope will dramatically improve the availability and accuracy of medical services.”

Google’s commitment to their vision is evident by their TensorFlow initiative. “TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.” Two recent papers describe in length the use of TensorFlow in retrospective studies (supported by Google AI) in which medical-images (from publicly accessed databases) where used:

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning, Nature Biomedical Engineering, Authors: Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng, and Dale R. Webster

As a demonstrator to the expected benefits the use of AI in interpretation of medical-imaging entails this is a very interesting paper. The authors show how they could extract information that is relevant for the assessment of the risk for having an adverse cardiac event from retinal fundus images collected while managing a totally different medical condition.  “Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic

blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70).”

 

Screenshot 2019-05-28 at 10.07.21Screenshot 2019-05-28 at 10.09.40

Clearly, if such algorithm would be implemented as a generalised and transferrable medical-device that can be used in routine practice, it will contribute to the cost-effectiveness of screening programs.

 

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography, Nature Medicine, Authors: Diego Ardila, Atilla P. Kiraly, Sujeeth Bharadwaj, Bokyung Choi, Joshua J. Reicher, Lily Peng, Daniel Tse , Mozziyar Etemadi, Wenxing Ye, Greg Corrado, David P. Naidich and Shravya Shetty.

This paper is in line of many previously published works demonstrating how AI can increase the accuracy of cancer diagnosis in comparison to current state of the art: “Existing challenges include inter-grader variability and high false-positive and false-negative rates. We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer. Our model achieves a state-of-the art performance (94.4% area under the curve) on 6,716 National Lung Cancer Screening Trial cases, and performs similarly on an independent clinical validation set of 1,139 cases.”

Screenshot 2019-05-28 at 10.22.06Screenshot 2019-05-28 at 10.23.48

The benefit of using an AI based application for lung cancer screening (If and when such algorithm is implemented as a generalised and transferable medical device) is well summarised by the authors: “The strong performance of the model at the case level has important potential clinical relevance. The observed increase in specificity could translate to fewer unnecessary follow up procedures. Increased sensitivity in cases without priors could translate to fewer missed cancers in clinical practice, especially as more patients begin screening. For patients with prior imaging exams, the performance of the deep learning model could enable gains in workflow efficiency and consistency as assessment of prior imaging is already a key component of a specialist’s workflow. Given that LDCT screening is in the relatively early phases of adoption, the potential for considerable improvement in patient care in the coming years is substantial. The model’s localization directs follow-up for specific lesion(s) of greatest concern. These predictions are critical for patients proceeding for further work-up and treatment, including diagnostic CT, positron emission tomography (PET)/CT or biopsy. Malignancy risk prediction allows for the possibility of augmenting existing, manually created interpretation guidelines such as Lung-RADS, which are limited to subjective clustering and assessment to approximate cancer risk.

BTW: The methods section in these two papers is detailed enough to allow any interested party to reproduce the study.

For the sake of balance-of-information, I would like to note that:

  • Amazon is encouraging access to its AI platform Amazon SageMaker “Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly. Amazon SageMaker is a fully-managed service that covers the entire machine learning workflow to label and prepare your data, choose an algorithm, train the model, tune and optimize it for deployment, make predictions, and take action. Your models get to production faster with much less effort and lower cost.” Amazon is offering training courses to help programmers get proficiency in Machine-Learning using its AWS platform: “We offer 30+ digital ML courses totaling 45+ hours, plus hands-on labs and documentation, originally developed for Amazon’s internal use. Developers, data scientists, data platform engineers, and business decision makers can use this training to learn how to apply ML, artificial intelligence (AI), and deep learning (DL) to their businesses unlocking new insights and value. Validate your learning and your years of experience in machine learning on AWS with a new certification.”
  • IBM is offering a general-purpose AI platform named Watson. Watson is also promoted as a platform to develop AI applications in the “health” sector with the following positioning: “IBM Watson Health applies data-driven analytics, advisory services and advanced technologies such as AI, to deliver actionable insights that can help you free up time to care, identify efficiencies, and improve population health.”
  • Microsoft is offering its AI platform as a tool to accelerate development of AI solutions. They are also offering an AI school : “Dive in and learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning, Visual Studio Code Tools for AI and Cognitive Toolkit. Our platform enables any developer to code in any language and infuse AI into your apps. Whether your solutions are existing or new, this is the intelligence platform to build on.”

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