If a Machine Could Predict Your Death, Should it? | Ziad Obermeyer | TEDxBoston
As an emergency doctor, I often find myself in the heartbreaking position of telling patients that they are much closer to death than they knew. Without that knowledge, and therefore without a plan for the kind of death they want, people often receive aggressive, uncomfortable medical care—even when they don’t want it. The ability to predict death is the stuff of myths and legends, but it’s much closer than we think: machine intelligence can provide precise predictions on a range of critical medical outcomes, and ease a great deal of suffering in the process. But do we really want those predictions? And what does better prediction with AI mean for the medical field? Ziad Obermeyer is an Assistant Professor at Harvard Medical School and a practicing emergency physician at the Brigham and Women’s Hospital, both in Boston.
His work uses machine learning to solve critical problems in clinical medicine. As patients get older and more complex, the volume of health data grows exponentially, and it becomes harder and harder for the human mind to keep up. Dr. Obermeyer’s work is focused on applying machine learning to find hidden signals in health data, and help doctors make better decisions and drive innovations in clinical research.
He is a recipient of an Early Independence Award from the NIH Common Fund, and a faculty affiliate at ideas42, Ariadne Labs, the Institute for Quantitative Social Science at Harvard University. He holds an A.B. (magna cum laude) from Harvard and an M.Phil. from Cambridge, and worked as a consultant at McKinsey & Co. in Geneva, New Jersey, and Tokyo, before returning to Harvard for his M.D (magna cum laude). This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
As an emergency doctor, I often find myself in the heartbreaking position of telling patients that they are much closer to death than they knew. Without that knowledge, and therefore without a plan for the kind of death they want, people often receive aggressive, uncomfortable medical care—even when they don’t want it. The ability to predict death is the stuff of myths and legends, but it’s much closer than we think: machine intelligence can provide precise predictions on a range of critical medical outcomes, and ease a great deal of suffering in the process. But do we really want those predictions? And what does better prediction with AI mean for the medical field? Ziad Obermeyer is an Assistant Professor at Harvard Medical School and a practicing emergency physician at the Brigham and Women’s Hospital, both in Boston.
His work uses machine learning to solve critical problems in clinical medicine. As patients get older and more complex, the volume of health data grows exponentially, and it becomes harder and harder for the human mind to keep up. Dr. Obermeyer’s work is focused on applying machine learning to find hidden signals in health data, and help doctors make better decisions and drive innovations in clinical research.
He is a recipient of an Early Independence Award from the NIH Common Fund, and a faculty affiliate at ideas42, Ariadne Labs, the Institute for Quantitative Social Science at Harvard University. He holds an A.B. (magna cum laude) from Harvard and an M.Phil. from Cambridge, and worked as a consultant at McKinsey & Co. in Geneva, New Jersey, and Tokyo, before returning to Harvard for his M.D (magna cum laude). This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
