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
People in this conversation
, influencing clinical practice and strengthening health systems
Curators: Stephen J. Williams, PhD, Dror Nir, PhD and Aviva Lev-Ari, PhD, RN
Series Content Consultant:
Larry H. Bernstein, MD, FCAP, Emeritus CSO, LPBI Group
Volume Content Consultant:
Prof. Marcus W. Feldman
https://www.youtube.com/watch?v=aT-Jb0lKVT8
BURNET C. AND MILDRED FINLEY WOHLFORD PROFESSOR IN THE SCHOOL OF HUMANITIES AND SCIENCES
Stanford University, Co-Director, Center for Computational, Evolutionary and Human Genetics (2012 – Present)
Latest in Genomics Methodologies for Therapeutics:
Gene Editing, NGS & BioInformatics,
Simulations and the Genome Ontology
2019
Volume Two
https://www.amazon.com/dp/B08385KF87
Product details
- File Size:3138 KB
- Print Length:217 pages
- Publisher:Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston; 1 edition (December 28, 2019)
- Publication Date:December 28, 2019
- Sold by:Amazon Digital Services LLC
- Language:English
- ASIN:B08385KF87
- Text-to-Speech: Enabled
- X-Ray:
- Word Wise:Not Enabled
- Lending:Enabled
- Enhanced Typesetting:Enabled
Prof. Marcus W. Feldman, PhD, Editor
Prof. Stephen J. Williams, PhD, Editor
and
Aviva Lev-Ari, PhD, RN, Editor
Introduction to Part 3: AI in Medicine – Voice of Aviva Lev-Ari & Professor Williams
There is a current consensus that of all specialties in Medicine, Artificial Intelligence technologies will benefit the most the specialty of Radiology.
What AI can do
Of course, there is still a lot AI can do for radiologists. Soonmee Cha, MD, neuroradiologist, has served as a program director at the University of California San Francisco since 2012 and currently oversees 100 radiology trainees, said at RSNA 2019 in Chicago
“we can see a future where AI is improving image quality, decreasing acquisition times, eliminating artifacts, improving patient communication and even decreasing radiation dose.
“If AI can detect when machines are being set up incorrectly and alert us, it’s a win for us and for patients,” she said.
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 Radiology, Radiology, Insights into Imaging and the Canadian Association of Radiologists Journal.
“Radiologists remain ultimately responsible for patient care and will need to acquire new skills to do their best for patients in the new AI ecosystem,” J. Raymond Geis, MD, ACR Data Science Institute senior scientist and one of the document’s leading contributors, said in a prepared statement. “The radiology community needs an ethical framework to help steer technological development, influence how different stakeholders respond to and use AI, and implement these tools to make the best decisions for—and increasingly with—patients.”
“The application of AI tools in radiological practice lies in the hand of the radiologists, which also means that they have to be well-informed not only about the advantages they can offer to improve their services to patients, but also about the potential risks and pitfalls that might occur when implementing them,” Erik R. Ranschaert, MD, PhD, president of EuSoMII. “This paper is therefore an excellent basis to improve their awareness about the potential issues that might arise, and should stimulate them in thinking proactively on how to answer the existing questions.”
Back in September, the Royal Australian and New Zealand College of Radiologists (RANZCR) published its own guidelines on the ethical application of AI in healthcare. The document, “Ethical Principles for Artificial Intelligence in Medicine,” is available on the RANZCR website.
https://www.radiologybusiness.com/topics/artificial-intelligence/radiology-societies-ethics-ai
Selective examples of applications of AI in the specialty of Radiology include the following:
- RSNA 2019, the world’s largest radiology conference, kicks off at Chicago’s McCormick Place on Sunday, Dec. 1, 2019, and promises to include more AI content than ever before. There will be an expanded AI Showcase this year, giving attendees access to more than 100 vendors in one location.
- “Artificial Intelligence and Precision Education: How AI Can Revolutionize Training in Radiology” | Monday, Dec. 2 | 8:30 – 10 a.m. | Room: E450A
- “Learning AI from the Experts: Becoming an AI Leader in Global Radiology (Without Needing a Computer Science Degree)” | Tuesday, Dec. 3 | 4:30-6 p.m. | Room: S406B
- “Deep Learning in Radiology: How Do We Do It?” | Wednesday, Dec. 4 | 8:30-10 a.m. | Room: S406B
- 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.
- AI model could help radiologists diagnose lung cancer
Michael Walter | November 27, 2019 | Medical Imaging
- AI a hot topic for radiology researchers in 2019
Michael Walter | November 26, 2019 | Medical Imaging
- GE Healthcare launches new program to simplify AI development, implementation
Michael Walter | November 26, 2019 | Business Intelligence
- How teleradiologists are helping underserved regions all over the world
Michael Walter | Medical Imaging Review
Sponsored by vRad, a MEDNAX Company
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
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)
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
AUDIT PODCASTS
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The perspective of what it truly means to be an AI company and AI platform.
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How MaxQ AI is reinventing the diagnostic process with AI in time sensitive, life threatening environments.
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How EnvoyAI is working towards a zero-click approach for physicians to feel confident in their findings.
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Recognizing the right questions to ask when training algorithms for more accurate results.
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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.
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
Dense Map of Artificial Intelligence Start ups in Israel
Image Source: https://www.startuphub.ai/multinational-corporations-with-artificial-intelligence-research-and-development-centers-in-israel/
(See here for an interactive version of the infographic above).
https://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
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
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
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
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
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
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
3.2.2 Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare
Curator: Stephen J. Williams, Ph.D.
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
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
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
3.2.9 An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
3.3 Clinical Aspects
Is AI ready for Medical Applications? – The Debate in August 2019 in Nature
Eric Topol (@EricTopol) |
|
Why I’ve been writing #AI for medicine is long on promise, short of proof nature.com/articles/s4159… @NatureMedicine status update in this schematic, among many mismatches pic.twitter.com/mpifYFwlp8 |
The “inconvenient truth” about AI in healthcare
npj Digital Medicine volume 2, Article number: 77 (2019)
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.
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
3.3.4 Live Coverage: MedCity Converge 2018 Philadelphia: AI in Cancer and Keynote Address
Reporter: Stephen J. Williams, PhD
3.3.5 VIDEOS: Artificial Intelligence Applications for Cardiology
Reporter: Aviva Lev-Ari, PhD, RN
3.3.6 Artificial Intelligence in Health Care and in Medicine: Diagnosis & Therapeutics
Reporter: Aviva Lev-Ari, PhD, RN
3.3.7 Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
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.
3.3.9 2019 Biotechnology Sector and Artificial Intelligence in Healthcare
Reporter: Aviva Lev-Ari, PhD, RN
3.3.10 Artificial intelligence can be a useful tool to predict Alzheimer
Reporter: Irina Robu, PhD
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
3.3.16 How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancer?
Author: Larry H Bernstein, MD, FCAP
3.3.17 An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
3.3.18 Artificial Intelligence and Cardiovascular Disease
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
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
3.3.21 Multiple Barriers Identified Which May Hamper Use of Artificial Intelligence in the Clinical Setting
Reporter: Stephen J. Williams, PhD.
3.3.22 Deep Learning–Assisted Diagnosis of Cerebral Aneurysms
Author and Curator: Dror Nir, PhD
3.3.23 Artificial Intelligence Innovations in Cardiac Imaging
Reporter: Aviva Lev-Ari, PhD, RN
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
3.4.2 HOTTEST Artificial Intelligence Hub: Israel’s High Tech Industry – Why?
Reporter: Aviva Lev-Ari, PhD, RN
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
3.4.5 IBM’s Watson Health division – How will the Future look like?
Reporter: Aviva Lev-Ari, PhD, RN
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
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.
3.4.8 Healthcare conglomeration to access Big Data and lower costs
Curator: Larry H. Bernstein, MD, FCAP
3.4.9 Linguamatics announces the official launch of its AI self-service text-mining solution for researchers.
Reporter: Aviva Lev-Ari, PhD, RN
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://www.cioapplications.com/news/making-a-breakthrough-in-drug-discovery-with-ai-nid-3114.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
3.4.13 The Health Care Benefits of Combining Wearables and AI
Reporter: Gail S. Thornton, M.A.
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.
3.4.15 Forbes Opinion: 13 Industries Soon To Be Revolutionized By Artificial Intelligence
Reporter: Aviva Lev-Ari, PhD, RN
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.
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.”
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
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
3.5.2.2 Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI
Reporter: Dror Nir, PhD
3.5.2.3 Showcase: How Deep Learning could help radiologists spend their time more efficiently
Reporter and Curator: Dror Nir, PhD
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
3.5.2.5 Applying AI to Improve Interpretation of Medical Imaging
Author and Curator: Dror Nir, PhD
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 Liu, Hossein Azizpour, Martin Eklund, Kevin Smith, Peter Lindholm, Fredrik Strand
Published Online: Dec 17 2019 https://doi.org/10.1148/radiol.2019190872
Results
A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P < .001), the dense area (mean, 38.2 cm2 vs 34.2 cm2; P < .001), and the percentage density (mean, 25.6% vs 24.0%; P < .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P < .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P < .001); this difference was most pronounced for more aggressive cancers.
Conclusion
Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers.
Related articles
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.
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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
AI in Healthcare 2020 Leadership Survey Report
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