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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 Arbor , United States , Jenna Wiens , Anna Stroup , Director Of Precision Health , Sloan Research Fellowship In Computer Science , University Of Michigan , Associate Professor Jenna , From Diagnosis , Clinical Decision Making , Jeans Link , Associate Professor , Computer Science , Precision Health , Innovators Under , Sloan Research Fellowship , ஆண்டு ஆர்பர் , ஒன்றுபட்டது மாநிலங்களில் , ஜென்னா வீனஸ் , பல்கலைக்கழகம் ஆஃப் மிச்சிகன் , இருந்து நோயறிதல் , மருத்துவ முடிவு தயாரித்தல் , இணை ப்ரொஃபெஸர் , கணினி அறிவியல் , ப்ரிஸிஶந் ஆரோக்கியம் , கண்டுபிடிப்பாளர்கள் கீழ் ,