Intelligent Medicine
Leaders in Biomedical Informatics Chart Roadmap for harnessing the promise of Medical AI
Improving the speed and accuracy of clinical diagnosis, augmenting clinical decision-making, reducing human error in clinical care, individualizing therapies based on a patient’s genomic and metabolomic profiles, differentiating benign from cancerous lesions with impeccable accuracy, identifying likely conditions a person may develop years down the road, spotting early tell-tale signs of an ultrarare disease, intercepting dangerous drug interactions before a patient is given a new medication, yielding real-time insights amidst a raging pandemic to inform optimal treatment of patients infected with a novel human pathogen.
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These are some of the promises that physicians and researchers look to fulfill using artificial intelligence — promises poised to transform clinical care, lead to better patient outcomes, and, ultimately, improve human lives.
Yet, AI is no silver bullet. It can fall prey to the cognitive fallibilities and blind spots of the humans who design it. AI models can be as imperfect as the data and clinical practices that the machine-learning algorithms are trained on, propagating the very same biases AI was designed to eliminate in the first place.
Beyond conceptual and design pitfalls, realizing the potential of AI also requires overcoming systemic hurdles that stand in the way of integrating AI-based technologies into clinical practice.
How does the field of medicine move forward to harness the promise of AI? How does it eliminate the perils posed by its suboptimal design or inappropriate use? How can AI be integrated seamlessly into frontline clinical care?
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These are some of the overarching questions that will be tackled at the inaugural Symposium on Artificial Intelligence for Learning Health Systems (SAIL) to be held Oct. 18–20 in Hamilton, Bermuda, an event envisioned to become an annual conference.
Conceived by Zak Kohane, chair of the department of biomedical informatics in the Blavatnik Institute at Harvard Medical School, the symposium will bring together the brightest minds from academia and industry in the fields of computer science, AI, clinical medicine, and healthcare. The charge is to establish both the philosophical and practical frameworks toward optimal translation of AI to the clinic.
“How can we bring the best aspects of AI to augment the best and most human components of the patient-clinician relationship and to safely accelerate 21st-century medicine? These are the central questions that I hope we will be able to answer,” Kohane said.
One of the more pedestrian — and more immediate — goals of the symposium, however, will be to bridge the chasms between various players in AI and medicine by simply having them talk with each other.
“We have various communities involved in AI all expecting AI to somehow improve medicine, but they have no way of communicating and engaging with one another. So one of the goals of this symposium is to create communication — not around theoretical issue of methodology but around the pragmatics of implementation,” Kohane said.
But the idea is not merely to have theoreticians and methodologists engage with practitioners. It is to create a common space for conversation — and eventual collaboration — among experts who have traditionally worked in parallel rather than at intersections, including clinical informaticians, machine learning specialists, clinicians, administrators, medical journal editors, and those charged with implementing AI in health care.
The event was originally scheduled to take place in 2020, but the COVID-19 pandemic derailed those plans.
To keep the momentum going and to lay the groundwork for the main event, Kohane held a virtual warm-up session in the fall of 2020, during which experts mapped some of the most acute challenges and greatest opportunities in the field of medical AI.
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OPENING REMARKS
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.
Moderator:
Jason Moore, Professor & Director of the Penn Institute for Biomedical Informatics, University of Pennsylvania
Speakers:
Michael Abramoff, Founder & CEO of IDX Technologies; Professor of Ophthalmology, University of Iowa
Marzyeh Ghassemi, Assistant Professor in Computer Science and Medicine, University of Toronto
Maia Hightower, Chief Medical Information Officer, University of Utah Health
Isaac Kohane, Professor & Chair of Biomedical Informatics, Harvard Medical School
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.
Moderator:
Nicholas Tatonetti, Associate Professor of Biomedical Informatics, Columbia University
Speakers:
Tiffani Bright, Biomedical Informatics Lead for the Center for AI, Research & Evaluation, IBM Watson Health
Greg Hager, Professor of Computer Science, Johns Hopkins University
Anant Madabhushi, Professor of Biomedical Engineering & Director of CCIPD, Case Western Reserve University
Cynthia Rudin, Professor of Computer Science, Electrical & Computer Engineering, & Statistical Science, Duke
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?
Moderator:
Charlotte Haug, International Correspondent, New England Journal of Medicine
Speakers:
Ken Ehlert, Chief Scientific Officer, UnitedHealth Group
Peter Lee, Corporate Vice President, Research and Incubation, Microsoft
Suchi Saria, Associate Professor & Director of AI and Health at Johns Hopkins; Founder & CEO, Bayesian Health
Eric Rubin, Editor-in-Chief, New England Journal of Medicine and NEJM Group
CLOSING REMARKS
2:55–3:00 PM ET
Speaker: Isaac Kohane, Professor & Chair of Biomedical Informatics, Harvard Medical School