A framework for shaping the future of AI in healthcare
Nigam Shah / Stanford University
Abstract:
In this session we will explore strategies for, and issues involved in, bringing Artificial Intelligence (AI) technologies to the clinic, safely and ethically. We will discuss the characteristics of a sound data strategy for powering a machine learning (ML) health system. The session introduces a frame-work for analyzing the utility of ML models in healthcare and discusses the implicit assumptions in aligning incentives for AI guided healthcare actions.
Bio: Dr. Nigam Shah is Associate Professor of Medicine (Biomedical Informatics) at Stanford University, and serves as the Associate CIO for Data Science for Stanford Health Care. Dr. Shah’s research focuses on combining machine learning and prior knowledge in medical ontologies to enable the learning health system. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and is inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.
Bio: Dr. Nigam Shah is Associate Professor of Medicine (Biomedical Informatics) at Stanford University, and serves as the Associate CIO for Data Science for Stanford Health Care. Dr. Shah’s research focuses on combining machine learning and prior knowledge in medical ontologies to enable the learning health system. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and is inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.