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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’s a critical need for machine learning in healthcare; however, the safe and meaningful adoption of these techniques will require collaboration between clinicians and AI

Ann arborUnited statesJenna wiensAnna stroupDirector of precision healthSloan research fellowship in computer scienceUniversity of michiganAssociate professor jennaFrom diagnosisClinical decision makingJeans linkAssociate professorComputer sciencePrecision healthInnovators underSloan research fellowship