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Archive for the ‘An executive’s guide to AI’ Category


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

Read Full Post »


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

Reporter: Stephen J. Williams, Ph.D.

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

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

Part of the report:

TECH GIANTS LEAD IN AI ACQUISITIONS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2019 Biotechnology Sector and Artificial Intelligence in Healthcare

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

Artificial Intelligence and Cardiovascular Disease

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

Top 12 Artificial Intelligence Innovations Disrupting Healthcare by 2020

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

 

Read Full Post »


50 CONTEMPORARY ARTIFICIAL INTELLIGENCE LEADING EXPERTS AND RESEARCHERS

 

Reporter: Aviva Lev-Ari, PhD, RN

SOURCE

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

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

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

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

MACHINE LEARNING
Hugo Larochelle
Ilya Sutskever
Matthew Zeiler
Michael I Jordan
Richard Socher
Ruslan Salakhutdinov
Ryan Adams
DEEP LEARNING
Adam Coates
Andrej Karpathy
Andrew Ng
Oriol Vinyals
Quoc V Le
Soumith Chintala
Timothy Lillicrap
Yann LeCun
Yoshua Bengio
NEURAL NETWORKS
Alex Graves
Alex Krizhevsky
Geoffrey E Hinton
RECURRENT NEURAL NETWORKS (RNN)
Jurgen Schmidhuber
REINFORCEMENT LEARNING
David Silver
Marcus Hutter   
Richard Sutton
GENERATIVE ADVERSARIAL NETWORKS (GAN)
Ian Goodfellow
COMPUTER VISION
Fei-Fei Li
Gary Bradski
Kaiming He
NATURAL LANGUAGE PROCESSING (NLP)
Dag Kittlaus
HEALTH TECH – BIOINFORMATICS
Daphne Koller
Sepp Hochreiter
ROBOTICS
Pieter Abbeel
Raia Hadsell
Raja Chatila
Tessa Lau
Wojciech Zaremba
DATA SCIENCE – BIG DATA – ALGORITHMS
Dez Blanchfield
Kirk Borne
Mike Tamir
Nando de Freitas
Pedro Domingos
Ronald van Loon
Randal S Olson
Toby Walsh
Zoubin Ghahramani
AI GAME DEVELOPMENT
Alex J. Champandard
Demis Hassabis
AI ETHICS
Joanna Bryson
Stuart Russell
SINGULARITY
Raymond “Ray” Kurzweil
MACHINE LEARNING FOR ARTISTS
Gene Kogan

Read Full Post »


The Regulatory challenge in adopting AI

Author and Curator: Dror Nir, PhD

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

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

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

Screenshot 2019-04-07 at 17.18.18

 

Acknowledgements

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

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

Author affiliations

Lev Tankelevitch, University of Oxford

Alice Ahn, University of Oxford

Rachel Paterson, University of Oxford

Matthew Reid, University of Oxford

Emily Hilbourne, University of Oxford

Bryan Adriaanse, University of Oxford

Giorgio Quer, Scripps Research Institute

Dror Nir, RadBee

Parth Patel, University of Cambridge

All affiliations are at the time of writing.

Polygeia

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

Foreword

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

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

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

Daniel Zeichner

MP for Cambridge

Chair, All-Party Parliamentary Group on Data Analytics

 

Executive summary

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

Data governance

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

Digital infrastructure

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

Standards

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

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

Regulations

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

The workforce

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

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

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

Legal liability

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

Recommendations

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

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

Reporter: Aviva Lev-Ari, PhD, RN

 

TOP TEN | ARTIFICIAL INTELLIGENCE 2018
The year’s most popular articles on artificial intelligence

An executive’s guide to AI

1. An executive’s guide to AI
Staying ahead in the accelerating artificial-intelligence race requires executives to make nimble, informed decisions about where and how to employ AI in their business. One way to prepare to act quickly: know the AI essentials presented in this guide. More →

Notes from the AI frontier: Applications and value of deep learning

2. Notes from the AI frontier: Applications and value of deep learning
An analysis of more than 400 use cases across 19 industries and nine business functions highlights the broad use and significant economic potential of advanced AI techniques. More →

What AI can and can’t do (yet) for your business

3. What AI can and can’t do (yet) for your business
Artificial intelligence is a moving target. Here’s how to take better aim. More →

4. The economics of artificial intelligence
Rotman School of Management professor Ajay Agrawal explains how AI changes the cost of prediction and what this means for business. More →

5. Notes from the AI frontier: Modeling the impact of AI on the world economy
Artificial intelligence has large potential to contribute to global economic activity. But widening gaps among countries, companies, and workers will need to be managed to maximize the benefits. More →

6. The executive’s AI playbook
It’s time to break out of pilot purgatory and more effectively apply artificial intelligence and advanced analytics throughout your organization. Our interactive playbook can help. More →

7. Artificial intelligence: Why a digital base is critical
Early AI adopters are starting to shift industry profit pools. Companies need strong digital capabilities to compete. More →

8. The promise and challenge of the age of artificial intelligence
AI promises considerable economic benefits, even as it disrupts the world of work. These three priorities will help achieve good outcomes. More →

9. The real-world potential and limitations of artificial intelligence
Artificial intelligence has the potential to create trillions of dollars of value across the economy—if business leaders work to understand what AI can and cannot do. More →

10. How artificial intelligence and data add value to businesses
Artificial intelligence will transform many companies and create completely new types of businesses. The cofounder of Coursera, AI Fund, and Landing.AI shares how businesses can benefit. More →

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Date: Thursday, January 3, 2019 at 3:03 PM

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Subject: Artificial Intelligence: 2018’s most popular articles

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