van der Schaar Lab

Turing Lecture: Transforming medicine through AI-enabled healthcare pathways

Advances in machine learning are set to transform medicine, yet considerable challenges exist. These include hard technical ones such as the complexity and quality of healthcare data, the need to consider multiple interactions between the diverse events over a patient’s life and the difficulty of estimating counterfactual outcomes. These challenges are exacerbated by practical ones – the requirement for cross-disciplinary collaboration, the delivery and transferability of any solutions between health systems at scale both nationally and internationally, the deployment of a service that provides tailored patient-level intelligence in near-real-time and most fundamentally, the absence of a way to conceptualise the complexity of healthcare to support interdisciplinary collaboration.

The speakers in this talk have joined forces to work on these challenges. We have combined a world-leading team in machine learning with the world’s largest, near-real time, high-quality cancer data collection service. Together we have built a test and development environment to create new methods for the near-real-time analysis of patient-level cancer data, that once tested, can then be rapidly integrated into the same national data collection service to benefit all patients.

In this June 3, 2019, talk we explain the background to our work and demonstrate how we have addressed some of these challenges. By using cancer as an exemplar, we aim to create a system-theoretical approach to healthcare that will help facilitate a deeper understanding of medicine and foster rigorous interdisciplinary working.

Introduction (Sir Alan Wilson, Director of Special Projects, The Alan Turing Institute)
Transforming medicine through AI-enabled healthcare pathways (Mihaela van der Schaar and Jem Rashbass)
Q&A (Chaired by Sir Alan Wilson)

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 and a Fellow at The Alan Turing Institute in London.

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.