Artificial Intelligence in Clinical Medicine: What Makes a Good Machine Learning Model for Clinical Applications?
Collin Stultz
January 26, 2022, Wednesday, 3:00 PM - 4:00 PM EST
Although applications of Machine Learning (ML) are now pervasive in the clinical literature, ML has yet to be embraced by the clinical community. So, what constitutes a good machine learning model for clinical applications? Certainly, a necessary condition for the success of any machine learning model is that it achieves an accuracy that is superior to pre-existing methods. In the healthcare sphere, however, accuracy alone does not, nor should it, ensure that a model will gain clinical acceptance. In view of the fact that no model, in practice, has 100% accuracy, attempts to understand when a given model is likely to fail should form an important part of the evaluation of any machine learning model that will be used clinically. Moreover, the most useful clinical models are explainable in the sense that it is possible to clearly articulate why the model arrives at a particular result for a given set of inputs. In this talk I will expand upon these challenges that make the creation of clinically useful ML models particularly difficult, and discuss ways in which they can be overcome.
Dr. Collin M. Stultz is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), a faculty member in the Harvard-MIT Division of Health Sciences and Technology, a Professor in the Institute of Medical Engineering and Sciences at MIT, a member of the Research Laboratory of Electronics (RLE), and an associate member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also a practicing cardiologist at the Massachusetts General Hospital (MGH). Dr. Stultz received his undergraduate degree in Mathematics and Philosophy from Harvard University; a PhD in Biophysics from Harvard University; and a MD from Harvard Medical School. He did his internship, residency, and fellowship at the Brigham and Women's Hospital in Boston. His scientific contributions have spanned multiple fields including computational chemistry, biophysics, and machine learning for cardiovascular risk stratification. He is a member of the American Society for Biochemistry and Molecular Biology and the Federation of American Societies for Experimental Biology and he is a past recipient of a National Science Foundation CAREER Award and a Burroughs Wellcome Fund Career Award in the Biomedical Sciences. Currently, research in his group is focused on the development of machine learning tools that can guide clinical decision making.