AI in Healthcare: Working Towards Positive Clinical Impact

Artificial intelligence (AI) applications in healthcare hold great promise, aiming to empower clinicians to diagnose and treat medical conditions earlier and more effectively. To ensure that AI solutions deliver on this promise, it is important to approach the design of prototype solutions with clinical applicability in mind, envisioning how they might fit within existing clinical workflows. Here we provide a brief overview of how we are incorporating this thinking in our research projects, while highlighting challenges that lie ahead.

Speaker bio: Nenad Tomasev: My research interests lie at the intersection of theory and impactful real-world AI applications, with a particular focus on AI in healthcare, which I have been pursuing at DeepMind since early 2016. In our most recent work, published in Nature in July 2019, we demonstrate how deep learning can be used for accurate early predictions of patient deterioration from electronic health records and alerting that opens possibilities for timely interventions and preventative care. Prior to moving to London, I had been involved with other applied projects at Google, such as Email Intelligence and the Chrome Data team. I obtained my PhD in 2013 from the Artificial Intelligence Laboratory at JSI in Slovenia, where I was working on better understanding the consequences of the curse of dimensionality in instance-based learning in many dimensions.