Making AI Impactful in Healthcare
There is increasing evidence that Machine Learning and Artificial intelligence algorithms can be used to enhance clinical care. In this talk, I address two critical aspects that can significantly improve the impact of such algorithms in healthcare practices: (1) moving beyond associations and creating algorithms capable of causal reasoning under ambiguity, and (2) a human-algorithm “centaur” model of care and decision-making, in which the power of human intuition is combined with the outstanding capabilities of algorithms. I describe our latest research on these subjects at the Public Impact Analytics Science Lab (PIAS-Lab) at Harvard, and discuss findings based on our various collaborations with the Mayo Clinic, Mass General Hospital, and some other public and private organizations.
Soroush Saghafian (Wikipedia) is an Associate Professor at Harvard University and is the founder and director of Harvard’s Public Impact Analytics Science Lab (PIAS-Lab). He also serves as a core faculty or a faculty affiliate for (a) Harvard Data Science Initiative, (b) Harvard Mossavar-Rahmani Center for Business and Government, (c) Harvard Center for Health Decision Science, (d) Harvard Ph.D. Program in Health Policy, (e) Harvard Belfer Center for Science and International Affairs, (f) Harvard Center for Public Leadership, and (g) Harvard Ariadne Labs (a pioneer lab in health systems innovation), and holds appointments at Massachusetts General Hospital (MGH), Beth Israel Deaconess Medical Center (BIDMC), and Mayo Clinic. He is an expert in healthcare AI, analytics, and operations management, and has collaborated with a variety of hospitals.
Dr. Saghafian's research has appeared numerous times in the news including in top national and international media outlets, and has been recognized through various awards, including the INFORMS MSOM Young Scholar Prize for “outstanding contributions to scholarship in operations management,” INFORMS MSOM Responsible Research Award (second place) for “contributing knowledge that may have implications for making the world a better place,” the Inaugural INFORMS Mehrotra Research Excellence Award “for significant contributions to the practice of health applications through operations research and management science modeling and methodologies,” INFORMS Computing Society Harvey Greenberg Award (honorable mention) “for research excellence in the field of computation and operations research applications, especially those in emerging application fields,” INFORMS Pierskalla Award “for the best research paper in healthcare,” INFORMS Franz Edelman Award (semi-finalist) “for achievement in advanced analytics, operations research, and management science,” and POMS College of Healthcare Best Paper Award. His forthcoming book with Cambridge University Press, “Insight-Driven Problem Solving: Analytics Science to Improve the World,” has been endorsed by top academic and industry figures [Full CV here].
]]>There is increasing evidence that Machine Learning and Artificial intelligence algorithms can be used to enhance clinical care. In this talk, I address two critical aspects that can significantly improve the impact of such algorithms in healthcare practices: (1) moving beyond associations and creating algorithms capable of causal reasoning under ambiguity, and (2) a human-algorithm “centaur” model of care and decision-making, in which the power of human intuition is combined with the outstanding capabilities of algorithms. I describe our latest research on these subjects at the Public Impact Analytics Science Lab (PIAS-Lab) at Harvard, and discuss findings based on our various collaborations with the Mayo Clinic, Mass General Hospital, and some other public and private organizations.
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