Revolutionizing Healthcare - second roundtable on interpretability in ML/AI for healthcare

Recording of the van der Schaar Lab's seventh Revolutionizing Healthcare engagement session for clinicians, which took place virtually on April 27, 2021.

This session was the second roundtable in a double-header focusing on interpretability in ML/AI for healthcare. Following a quick introduction by Mihaela van der Schaar, a panel of four clinicians and the audience of clinicians discussed a range of complex issues surrounding interpretability, including whether or not current expectations among the clinical community are realistic.

Introduction - 0:00​
Meet the roundtable panelists - 2:13
Declaration of interests - 3:07
Mihaela's presentation on terminology and types of interpretability - 4:02
Initial questions for Mihaela on interpretability methods [Aneeq Rehman] - 13:10
Question for Mihaela on generating hypotheses through interpretable ML [David Chong] - 16:09
Question for panelists expectations for ML versus clinical scoring systems [David Chong] - 18:20
Discussion with panelist Maxime Cannesson: "All boxes are black" - 23:34
Discussion among panelists: expectations from ML models - 32:48
Question for panelists on knowledge gaps among personnel [Harpreet Sood] - 44:29
Question for panelists on preparing clinicians to improve healthcare outcomes [Timing Liu] - 49:19
Question for panelists on expectations of ML interpretability versus humans [Venkat Reddy] - 55:15
Intro to next sessions and note on CPD credits - 1:06:28

NOTE: This information was up-to-date at the time of the presentation but does not take into account material published since then.