Augmenting Biomedical Intelligence through Generative Models As Observatories & Labs

By: James Evans, Max Palevksy Professor of Sociology & Data Science, Director of Knowledge Lab, and Founding Faculty Director of Computational Social Science at the University of Chicago

Abstract:

In this talk I explore a range of evolving opportunities for artificial intelligence (AI)-driven generative models to accelerate biomedical discovery and knowledge, medical innovation, and the enhancement of human health. Specifically, I explore how generative AI models can be both used as observatories of biomedical reality, and virtual laboratories of biomedical intervention. I begin by investigating the design of generative models for adaptive and automated scientific exploration and the creation of knowledge maps for prospecting and recognizing the significance of new biomedical discoveries. I then examine models that improve the robustness of biomedical knowledge on which future discoveries may most profitably build. Next, I survey the use of patient records linked with biological data to construct generative models of human disease for predictive diagnosis and treatment. Finally, I describe AI opportunities for improving human health and safety, including the predictive distribution of health care and public safety personnel.

Watch the Video of Evans’ Keynote Presentation:

Bio:

James Evans is the Max Palevksy Professor of Sociology & Data Science, Director of Knowledge Lab, and Founding Faculty Director of Computational Social Science at the University of Chicago, the Santa Fe Institute, and Google. Evans’ research uses large-scale data, machine learning and generative models to understand how collectives of humans and machines think and what they know about science, technology, and medicine. This involves inquiry into the emergence of ideas, shared patterns of reasoning, and processes of attention, communication, agreement, and certainty. Thinking and knowing collectives like science and medicine, large language models, the Web, or modern commercial enterprises involve complex networks of diverse human and machine intelligences, collaborating and competing to achieve overlapping aims. Evans’ work connects the interaction of these agents with the knowledge they produce and its value for themselves and the system. His work is supported by numerous federal agencies (NSF, NIH, DOD), foundations and philanthropies, has been published in Nature, Science, PNAS, and top social and computer science outlets, and has been covered by global news outlets from the New Yorker, Wall Street Journal, Atlantic, and New York Times to the Economist, Le Monde, El Pais, and Die Zeit.