van der Schaar Lab

Revolutionizing Healthcare: roundtable on interpretability in ML/AI for healthcare

The van der Schaar Lab’s sixth Revolutionizing Healthcare engagement session took place virtually on March 31, 2021, and was attended by over 40 healthcare professionals (primarily clinicians).

The focus of the session was interpretability in ML/AI for healthcare. Following a presentation by Mihaela van der Schaar, a panel of four clinicians discussed various definitions and types of interpretability, as well as real-world needs and contexts in healthcare settings.

A follow-up roundtable will be held on April 27. Details can be found here.

Introduction – 0:00​​
Meet the roundtable panelists – 2:04
Declaration of interests – 3:08
Mihaela’s presentation – 4:01​​
Roundtable topic 1 [defining interpretability] – 25:08
Roundtable topic 2 [types of interpretability] – 34:52
Roundtable topic 3 [suitability of linear models] – 48:36
Roundtable topic 4 [interpretability in context] – 54:22
Intro to next sessions and note on CPD credits – 1:05:13

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

Sign up for our upcoming sessions here.

Nick Maxfield

Nick Maxfield

Nick oversees the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Nick studied Japanese (BA Hons.) at the University of Oxford, graduating in 2012. Nick previously worked in HQ communications roles at Toyota (2013-2016) and Nissan (2016-2020).

Given his humanities/languages background and experience in communications, Nick is well-positioned to highlight and explain the real-world impact of research that can often be quite esoteric. Thankfully, he is comfortable asking almost endless questions in order to understand a topic.

Mihaela van der Schaar

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.