Revolutionizing Healthcare - how can AI/ML transform organ transplantation?

Описание: Recording of the van der Schaar Lab's twelfth Revolutionizing Healthcare engagement session for clinicians, which took place virtually on November 30, 2021.

In this session, Mihaela van der Schaar and a panel of 4 international clinicians explored possible AI and machine learning approaches to organ transplantation.

In the first part of the session, Dr. Alexander Gimson, a Cambridge-based transplant hepatologist, outlined the importance, complexities, and unique challenges of the organ transplantation setting, while also highlighting some recent collaborative projects using machine learning for donor-recipient matchmaking and survival prediction. The latter part of the session featured a clinician roundtable comprising an expert panel of four transplantation specialists. Our panelists answered an array of questions (most of which came from the audience) and discussed the path forward for AI and machine learning for organ transplantation.

Introduction - 0:00​
Declaration of interests - 0:38
Session overview - 1:42
Meet the roundtable panelists - 4:20
Presentation by Mihaela: "Getting ML-powered tools into the hands of clinicians - part 3" - 6:02
Alexander Gimson: "AI for transplantation: initial results, future proposals" - 13:24
Alexander Gimson: OrganBoard demonstrator and case studies - 29:09
Roundtable topic 1: what should we focus on next? - 33:26
Roundtable topic 2: how has organ transplantation evolved in recent years? - 35:34
Roundtable topic 3: how can we empower surgeons in the transplant setting? - 39:40
Roundtable topic 4: rethinking standards for measuring outcomes - 47:00
Roundtable topic 5: sensitivity to patient trajectory - 50:29
Roundtable topic 6: taking into account regional variation in transplant practices - 58:05
Roundtable topic 7: determining benefit of transplantation vs. conservative approaches - 1:01:01
Intro to next sessions and note on CPD credits - 1:06:22

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

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