From Diagnosis to Treatment: Augmenting Clinical Decision Making with Artificial Intelligence
Jenna Wiens
December 9, 2020, Wednesday, 3:00 PM - 4:00 PM EDT
Abstract
Though the potential of artificial intelligence (AI) in healthcare warrants genuine enthusiasm, meaningful impact will require careful integration into clinical care. AI tools are susceptible to mistakes and rarely capable of capturing all of the nuances ,pertaining to a complex clinical situation. Thus, we propose approaches designed to augment, rather than replace, clinicians during clinical decision making. In this talk, I will highlight two related research directions in which we propose i) a transfer learning approach for mitigating potentially harmful shortcuts when making diagnoses and ii) a novel reinforcement learning approach for matching patients to treatments. In summary, there is a critical need for machine learning in healthcare; however, the safe and meaningful adoption of these techniques will require collaboration between clinicians and AI.
Bio
Jenna Wiens is an Associate Professor of Computer Science and Engineering (CSE) at the University of Michigan in Ann Arbor. She currently heads the MLD3: Machine Learning for Data-Driven Decisions research group. Her primary research interests lie at the intersection of machine learning and healthcare. She is particularly interested in time-series analysis, transfer/multitask learning, and causal inference. The overarching goal of her research agenda is to develop the computational methods needed to help organize, process, and transform data into actionable knowledge.

She received her PhD in 2014 from MIT. At MIT, she worked with Prof. John Guttag in the Computer Science and Artificial Intelligence Lab (CSAIL). Her PhD research focused on developing accurate patient risk-stratification approaches that leverage spatiotemporal patient data, with the ultimate goal of discovering information that can be used to reduce the incidence of healthcare-associated infections. In 2015, she was named Forbes 30 under 30 in Science and Healthcare; she received an NSF CAREER Award in 2016; in 2017 she was named to the MIT Tech Review's list of 35 Innovators Under 35; and most recently she received a Sloan Fellowship in Computer Science.