An artificial intelligence method for fast diagnosis of rare disease
Francisco M. De La Vega, Barry Moore, Erwin Frise, Shimul Chowdhury, Narayanan Veeraraghavan, Stephen F. Kingsmore, Martin G. Reese, and Mark Yandell
Deep learning assistance for physician diagnosis of tuberculosis
using chest x-rays in HIV patients
Pranav Rajpurkar, Chloe O’Connell, Amit Schechter, Nishit Asnani, Jason Li, Amirhossein Kiani, Robyn L. Ball, Marc Mendelson, Gary Maartens, Daniël J. van Hoving, Rulan Griesel, Andrew Y. Ng, Tom H. Boyles & Matthew P. Lungren
Laura Early Warning System (LEWS)
Hugo Morales, Jhonatan Kobylarz, Mateus Cichelero, Felipe Barletta, Cristian Rocha
Deep Learning to Predict Renal Outcomes Solely Based on
Kidney Ultrasound Images
Mandy Rickard, Lauren Erdman, Marta Skreta, Daniel T. Keefe, Michael Brudno, Armando J. Lorenzo, Anna Goldenberg
System for High Intensity EvaLuation During Radiation Therapy
Julian C. Hong, Neville C.W. Eclov, Nicole Dalal, Samantha M. Thomas, Sarah J. Stephens, Mary Malicki, Stacey Shields, Alyssa Cobb, Yvonne M. Mowery, Donna Niedzwiecki, Jessica D. Tenenbaum, Manisha Palta
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Oct 21, 2020 Program
12:00 - 12:05 PM ET
Speaker: Jessie Tenenbaum, Chief Data Officer, North Carolina Department of Health and Human Services
PANEL 1 - Getting smarter about evaluating AI
12:05 - 12:55 PM ET
Using artificial intelligence solutions at the point of care with a focus on the adoption, evaluation, and vetting process.
PANEL 2 - New frontiers in healthcare AI
1:00 - 1:55 PM ET
Imagining solutions based on real-world experience, inspiring the next generation and discussing the most significant challenges in AI and medicine.
PANEL 3 - How today’s care is being transformed by AI
2:00 - 2:55 PM ET
Maximizing the impact of AI as a mainstream diagnostic and therapeutic. Where are the possibilities, and what are the possible pitfalls?
2:55 - 3:00 PM ET
Speaker: Isaac Kohane, Professor & Chair of Biomedical Informatics, Harvard Medical School
Speakers & Panelists
Michael AbramoffCEO, IDx Technologies
Dr. Michael D. Abramoff, MD, PhD, is a fellowship-trained retina specialist, computer scientist and entrepreneur. He is Founder and CEO of IDx Technologies, Inc., the first company ever to receive FDA clearance for an autonomous AI diagnostic system. In this capacity, as an expert on AI in healthcare, he has been invited to brief the US Congress, the White House, the U.S. Food and Drug Administration (FDA) and the Federal Trade Commission (FTC).
In 2010, Dr. Abramoff’s research findings led him to found IDx Technologies Inc. to bring to patients more affordable, more accessible, and higher quality healthcare. IDx is a leading AI diagnostics company on a mission to transform the affordability, quality, and accessibility of healthcare. The company is focused on developing clinically-aligned autonomous AI systems. By enabling diagnostic assessment in primary care settings, IDx aims to increase patient access to high-quality, affordable disease detection. In 2018, IDx became the first company ever to receive FDA clearance for an autonomous AI diagnostic system. The system, called IDx-DR, detects diabetic retinopathy without requiring a physician to interpret the results, and has been implemented widely in primary care clinics all over the US and in Europe.
As a physician-scientists, Dr. Abramoff continues to treat patients with retinal disease and trains medical students, residents, and fellows, as well as engineering graduate students at the University of Iowa. Dr. Abramoff has published over 270 peer reviewed journal papers (h-index 59) on AI, image analysis, and retinal diseases, that have been cited over 28,000 times, as well as many book chapters. He is the inventor on US 17 patents and 5 patent applications on AI, medical imaging and image analysis.
Tiffani BrightBiomedical Informatics Evaluation Team Lead, IBM Watson health
Tiffani J. Bright, PhD is the Biomedical Informatics Evaluation Team Lead for the Center for AI, Research, and Evaluation at IBM Watson Health. She has deep expertise and experience in the development and evaluation of health information technologies and clinical decision support. Dr. Bright’s work in academia, government, and industry has contributed scientific methods and rich research findings about systems’ usability, adoption, and impact across diverse settings and end-users. As the Qualitative Team Lead and Design Team Liaison, Dr. Bright provides scientific expertise and leadership in the design, development, and evaluation of IBM Watson Health solutions and manages scientific activities in collaboration with academic, non-profit, and client partners across the world. Dr. Bright is also responsible for a 10-year, $25 million research collaboration between IBM Watson Health and Vanderbilt University Medical Center to advance the science of AI in healthcare. Additionally, she co-leads IBM Watson Health’s Achieve Equity Initiative and serves on the IBM Watson Health Diversity Council.
Dr. Bright is the recipient of several awards and honors, including a Meyerhoff Scholarship from the University of Maryland Baltimore County (UMBC), a National Library Medicine Biomedical Informatics Predoctoral Fellowship, and a Columbia University Center for Interdisciplinary Research to Reduce Antimicrobial Resistance Predoctoral Fellowship. She is an elected board member of the American Medical Informatics Association (AMIA) and has held leadership appointments on numerous committees throughout AMIA and the STEM communities. Currently, she is chairing the newly established AMIA Diversity, Equity, and Inclusion Task Force. She received her BA degree in sociology from The College of William and Mary, BS degree in information systems from UMBC, PhD degree in biomedical informatics from Columbia University, and completed her postdoctoral fellowship in the Division of Clinical Informatics at Duke University.
Ken EhlertChief Scientific Officer, UnitedHealth
Marzeyeh GhassemiAssistant Professsor, University of Toronto
Dr. Marzyeh Ghassemi is an Assistant Professor at the University of Toronto in Computer Science and Medicine, and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. She currently serves as a NeurIPS 2019 Workshop Co-Chair, and Board Member of the Machine Learning for Health Unconference. Previously, she was a Visiting Researcher with Alphabet's Verily and a post-doc with Dr. Peter Szolovits at MIT (CV). Professor Ghassemi has a well-established academic track record in personal research contributions across computer science and clinical venues, including KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, EMBC, Nature Translational Psychiatry, Critical Care, and IEEE Transactions on Biomedical Engineering. She is an active member of the scientific community, reviewing for NeurIPS, ICML, KDD, MLHC, AAAI, AMIA/AMIA-CRI, and JMLR. She has co-organized the 2016/2017/2018 NeurIPS Workshop on Machine Learning for Health (ML4H), been 2018 Area Chair for MLHC, and served as an Academic Guest Editor on the 2018 PLoS ONE call on Machine Learning in Health and Biomedicine.
Professor Ghassemi's PhD research at MIT focused on creating and applying machine learning algorithms towards improved prediction and stratification of relevant human risks with clinical collaborations at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, encompassing unsupervised learning, supervised learning, and structured prediction. Her work has been applied to estimating the physiological state of patients during critical illnesses, modeling the need for a clinical intervention, and diagnosing phonotraumatic voice disorders from wearable sensor data. Prior to MIT, Marzyeh received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.
Gregory HagerProfessor, Johns Hopkins University
Charlotte HaugInternational Correspondent, NEJM
Charlotte J. Haug is a MD, PhD from the University of Oslo, Norway and a MSc in Health Services Research from Stanford University, USA. She is International Correspondent at the New England Journal of Medicine, Senior Scientist at SINTEF Digital Health (Norway), and Adjunct Affiliate of Stanford Health Policy, Stanford University.
Dr Haug has worked in clinical medicine and research in Norway and with organization, priority setting and supervision of healthcare systems both nationally and internationally. From 2002 - 2015, she was Editor-in-Chief of the Journal of the Norwegian Medical Association and a member of the International Committee of Medical Journal Editors (ICMJE, the “Vancouver group”). She was a Council Member of the Committee on Publication Ethics (COPE) from 2005-2015 and Vice-Chair of COPE from 2012-2015. She received the Council of Science Editors (CSE) Award for Meritorious Achievement in 2013, and was on the International Advisory Board of the 4th World Conference on Research Integrity in Rio de Janeiro, Brazil, in 2015. She has worked extensively with issues concerning scientific publication, research and publication ethics with a particular emphasis on how to handle and optimize the use personal data collected in clinical and clinical research settings while preserving the individuals’ right to privacy.
Maia HightowerCMIO, University of Utah Health
Maia Hightower, MD, MPH, MBA is the Chief Medical Information Officer for The University of Utah Health. She joined the UUH team in March of 2019.
Prior to joining the University of Utah Health team, she was the Chief Medical Information Officer and Interim Chief Population Health Officer for The University of Iowa Health Care. She joined the faculty of the University of Iowa’s Carver College of Medicine, Department of Internal Medicine in August of 2015, after serving as Associate Medical Director for Stanford Health Care’s University Healthcare Alliance. Dr. Hightower received her Medical Degree, as well as a Master of Public Health, from the University Of Rochester School Of Medicine, followed by residencies in Internal Medicine and Pediatrics at the University of California, San Diego. She also holds an MBA from the University of Pennsylvania’s Wharton School.
As Chief Medical Information Officer at two world class academic medical health systems, she has lead or collaborated on a number of initiatives ranging from EMR optimization and usability, introduction of new technology and population health initiatives, process improvement, and implementation of AI in clinical operations. She leads the University of Utah’s Enterprise Data Warehouse which provides a complete suite of data and analytic end-to-end solutions for operations and research purposes. She strives to “leave no one behind” with a team-based approach to supporting all members of the healthcare community to achieve professional fulfillment in our rapidly changing digital healthcare environment.
Dr. Hightower has been recognized twice by Health Data Management as one of the “Most Powerful Women in Healthcare IT” and “25 leading CMIOs at healthcare organizations.” She was recognized by Becker’s Hospital Review as one of “50 hospital and health system CMIOs to know 2017”.
Dr. Hightower’s professional interests include healthcare leadership, clinical informatics, clinical decision making and the evolution of medical care in the digital era. She believes that diverse teams create and innovate. Her drive is to advance the work of clinicians, care teams, researchers, educators and learners by providing health information technology tools, processes, and systems needed to adapt to change and provide value to our patients, families and communities in the digital era of healthcare.
She enjoys spending time with family in the great outdoors and is an avid skier, tennis player and runner. She loves gadgets of all kinds and can be found wearing more than one fitness tracker at all times.
Isaac KohaneProfessor, Harvard Medical School
Peter LeeCorporate Vice President, Research and Incubation, Microsoft
Dr. Peter Lee is Corporate Vice President, Research and Incubation, at Microsoft. In this role, he leads research in the eight Microsoft Research locations around the world, as well as the incubation of new research-powered products and lines of business, the largest of which today is the company’s emerging healthcare and life sciences business. Dr. Lee has extensive experience in managing the process of going from fundamental research to commercial impact in a wide range of areas, from artificial intelligence, to quantum computing, to biotechnology, and more. Before joining Microsoft in 2010, he was at DARPA, where he established a new technology office that created operational capabilities in machine learning, data analysis, and social science. From 1987 to 2005 he was a Professor at Carnegie Mellon University, and from 2005 to 2008 the Head of the university’s computer science department.
Today, in addition to his management responsibilities, Dr. Lee speaks and writes widely on technology trends and policies. He is a member of the National Academy of Medicine and serves on the Boards of Directors of the Allen Institute for Artificial Intelligence, the Brotman Baty Institute for Precision Medicine, and the Kaiser Permanente Bernard J. Tyson School of Medicine. In public service, Dr. Lee was a commissioner on the President Obama’s Commission on Enhancing National Cybersecurity, led studies for the National Academies on the impact of federal research investments on economic growth, and testified before both the US House Science and Technology Committee and the US Senate Commerce Committee.
Anant MadabhushiProfessor, Case Western Reserve University
Dr. Anant Madabhushi is Director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD); F. Alex Nason Professor II, Department of Biomedical Engineering; and on faculty in the Departments of Pathology, Radiology, Radiation Oncology, Urology, General Medical Sciences, Electrical, Computer, and Systems Engineering, and Computer and Data Sciences at Case Western Reserve University. He is also a Research Health Scientist at the Louis Stokes, Cleveland Veterans Administration Medical Center. Dr. Madabhushi has authored more than 400 peer-reviewed publications and more than 100 patents either issued or pending in the areas of artificial intelligence, radiomics, medical image analysis, computer-aided diagnosis, and computer vision.
He is a Wallace H. Coulter Fellow, a Fellow of the American Institute of Medical and Biological Engineering (AIMBE), and a Fellow of the Institute for Electrical and Electronic Engineers (IEEE). In 2015, he was named by Crain’s Cleveland Business as one of “Forty under 40” making positive impact to business in Northeast Ohio. In 2017, he received the IEEE Engineering in Medicine and Biology Society (EMBS) award for technical achievements in computational imaging and digital pathology. His work on "Smart Imaging Computers for Identifying lung cancer patients who need chemotherapy" was called out by Prevention Magazine as one of the top 10 medical breakthroughs of 2018. In 2019, Nature Magazine hailed him as one of 5 scientists developing "offbeat and innovative approaches for cancer research". In 2019 and 2020, Dr. Madabhushi was named to The Pathologist’s Power List of 100 inspirational and influential professionals in pathology.
Jason MooreProfessor, University of Pennsylvania
Jason Moore is the Edward Rose Professor of Informatics and Director of the Penn Institute for Biomedical Informatics. He also serves as Senior Associate Dean for Informatics and Chief of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics. He came to Penn in 2015 from Dartmouth where he was Director of the Institute for Quantitative Biomedical Sciences. Prior to Dartmouth he served as Director of the Advanced Computing Center for Research and Education at Vanderbilt University where he launched their first high-performance computer. He has a Ph.D. in Human Genetics and an M.S. in Applied Statistics from the University of Michigan. He leads an active NIH-funded research program focused on the development of artificial intelligence and machine learning algorithms for the analysis of complex biomedical data. He is an elected fellow of the American College of Medical Informatics (ACMI), and an elected fellow of the American Statistical Association (ASA), and an elected fellow of the American Association for the Advancement of Science (AAAS). He serves as Editor-in-Chief of the journal BioData Mining.
Eric RubinEditor-in-Chief, NEJM
Eric J. Rubin, M.D., Ph.D., joined the New England Journal of Medicine (NEJM) and NEJM Group as Editor-in-Chief in September 2019, taking on the responsibility for oversight of all editorial content and policies.
Dr. Rubin is an Associate Physician specializing in infectious disease at Brigham and Women’s Hospital and is a Professor in the Department of Immunology and Infectious Diseases at the Harvard T.H. Chan School of Public Health. He serves on several scientific advisory boards to groups interested in infectious disease therapeutics. Dr. Rubin has also previously served as the Associate Editor for Infectious Disease at the New England Journal of Medicine as well as an editor for several basic science journals including PLoS Pathogens, Tuberculosis, and mBio.
Cynthia RudinProfessor, Duke University
Suchi SariaAssociate Professor, Johns Hopkins
Suchi Saria is the John C. Malone Associate Professor of computer science, statistics and health policy, the Director of the Machine Learning and Healthcare Lab, and the founding Research Director of the Malone Center for Engineering in Healthcare at Johns Hopkins. She is also the founder of Bayesian Health, an AI platform company building workflow-integrated clinical AI tools for improving health outcomes. Her research focuses on developing next generation diagnostic and treatment planning tools that leverage statistical methods to individualize care. Towards this, her methodological work focuses on questions such as: How can we support decision-making in safety-critical domains? How can we combine different sources of information with prior knowledge to derive actionable and trustworthy inferences? How can we characterize and improve reliability of the resulting inferences in challenging real-world settings? On the translational front, her work first demonstrated the use of machine learning to make early detection possible in sepsis, a life-threatening condition (Science Trans. Med. 2015). In Parkinson's, her work showed a first demonstration of using readily-available sensors to easily track and measure symptom severity at home, to optimize treatment management (JAMA Neurology 2018).
She is passionate about scaling innovations in healthcare delivery and is an active advisor to groups innovating on this front including Scripps Research Translational Institute, Patient Ping (enabling care coordination through real-time data) and Duality Technologies (building privacy-sensitive ML). Her work has received recognition in various forms including best paper awards at machine learning, informatics, and medical venues, a Rambus Fellowship (2004-2010), an NSF Computing Innovation Fellowship (2011), selection by IEEE Intelligent Systems to Artificial Intelligence’s “10 to Watch” (2015), the DARPA Young Faculty Award (2016), MIT Technology Review’s ‘35 Innovators under 35’ (2017), the Sloan Research Fellowship (2018), National Academy of Medicine's Emerging Leaders in Health and Medicine (2018), and the World Economic Forum Young Global Leader (2018). In 2017, her work was among four research contributions presented by Dr. France Córdova, Director of the National Science Foundation to Congress’ Commerce, Justice Science Appropriations Committee. She is on the editorial board of multiple journals including the flagship AI journal---the Journal of Machine Learning Research. Saria joined Hopkins in 2012. Prior to that, she received her PhD from Stanford University working with Prof. Daphne Koller.
Nick TatonettiAssociate Professor, Columbia University
Jessie TenenbaumChief Data Officer, North Carolina DHHS
Jessie Tenenbaum serves as the Chief Data Officer (CDO) for DHHS, assisting the Department in developing a strategy to use information to inform and evaluate policy and improve the health and well-being of residents of North Carolina.
Prior to taking on the role of CDO, Tenenbaum was a founding faculty member of the Division of Translational Biomedical Informatics within Duke University's Department of Biostatistics and Bioinformatics. Her research applies expertise in data standards and electronic health records to stratify mental health disorders to enable precision medicine. She is also interested in ethical, legal and social issues around big data and precision medicine. Prior to her faculty role, Tenenbaum was Associate Director for Bioinformatics for the Duke Translational Medicine Institute, with a focus on data standards and enterprise data warehousing.
Nationally, Tenenbaum is a member of the Board of Directors for the American Medical Informatics Association (AMIA) and plays a leadership role in AMIA's Committee on Women in Informatics. She is an Associate Editor for the Journal of Biomedical Informatics and serves on the Board of Scientific Counselors for the National Library of Medicine. She also serves on a number of editorial and advisory boards including Nature Scientific Data and Briefings in Bioinformatics.
After earning her bachelor’s degree in biology from Harvard University, Tenenbaum was a Program Manager at Microsoft Corp. in Redmond, Wash., for six years before pursuing a PhD in biomedical informatics at Stanford University. As a 2006 Science Policy Fellow at the National Academy of Medicine (then the Institute of Medicine), she helped to organize the first Roundtable on Evidence-Based Medicine and assisted in early planning stages for a workshop on health information technology.
Tenenbaum is a strong promoter and advocate of young women interested in in STEM (science, technology, engineering and math) careers. She volunteers in local schools to teach programming to elementary school students and volunteers with Triangle Women in STEM to plan annual STEM Day events for girls.