Medical AI, Salesforce Research
Guest Moderator, Cornell Tech
Frontiers of Medical AI: Therapeutics and Workflows
As the artificial intelligence and deep learning revolutions have swept over a number of industries, medicine has stood out as a prime area for beneficial innovation. The maturation of key areas of AI – computer vision, natural language processing, etc. – have led to their successive adoption in certain application areas of medicine. The field has seen thousands of researchers and companies begin pioneering new and creative ways of benefiting healthcare with AI. Here we’ll discuss two vitally important areas – therapeutics, and workflows. In the space of therapeutics we’ll discuss how multi-modal AI can support physicians in complex decision making for cancer treatments, and how natural language processing can be repurposed to create custom-generated proteins as potential therapeutics. Within workflows, we’ll explore how to build a COVID-specialized search engine, and discuss ways in which this could empower health systems to securely, and accurately, search over their highly sensitive data.
Andre Esteva is a researcher and entrepreneur in deep learning and computer vision. He currently serves as Head of Medical AI at Salesforce Research. Notably, he has led research efforts in AI-enabled medical diagnostics, and therapeutic decision making. For instance, his work has shown that computer vision algorithms can match and exceed the performance of top physicians at diagnosing cancers from medical imagery. Expanded into video they can diagnose behavioral conditions like autism.
In the space of AI-enabled therapeutics, his research leverages multi-modal datasets to train AI models that can personalize oncology treatments for patients by determining the best course of therapy for them. He has published numerous review articles, and co-authored the standardized guidelines for AI in clinical trials. His work has made the covers of Nature and Nature Medicine, as well as Cell, The Lancet, NeurIPS, and other venues, while being widely covered by the WSJ, Fortune, BBC, The Economist, and hundreds of other news outlets. He has worked at Google Research, Sandia National Labs, and GE Healthcare, and has co-founded two tech startups.
He obtained his PhD in Artificial Intelligence at Stanford, where he worked with Sebastian Thrun, Jeff Dean, Stephen Boyd, Fei-Fei Li, and Eric Topol. His research was in deep learning and computer vision, with applications built for dermatology, drug screening, neuroscience, and psychiatry. He finished undergraduate degrees with highest honors in Electrical Engineering and Pure Math from UT-Austin and was awarded Engineering Valedictorian (formally, the Outstanding Scholar-Leader Award).