AI in Healthcare Capstone

Laurence Baker, Ph.D., is the Bing Professor of Human Biology and a distinguished member of the Health Policy group at Stanford University, where he applies his expertise in economic and statistical analysis to tackle pressing challenges within the healthcare system. As an accomplished health economist, Professor Baker teaches both undergraduate and graduate courses and has published extensively on various healthcare financing and system issues. His research delves into the impacts of financial incentives, technological advancements in medicine, competition in healthcare markets, and the dynamics of managed care and insurance plans. In addition to his primary role at Stanford, he is a Professor of Economics (by courtesy), a Senior Fellow at the Stanford Institute for Economic Policy Research, and a Research Associate at the National Bureau of Economic Research. A former Chair of the Department of Health Research and Policy at Stanford, Professor Baker has received numerous accolades, including the ASHE Medal from the American Society of Health Economists and the Alice S. Hersh Young Investigator Award from AcademyHealth. He holds a Ph.D. in Economics from Princeton University and a B.A. in Economics and Mathematics from Calvin College.

Dr. Nigam Shah is an esteemed Associate Professor of Medicine specializing in Biomedical Informatics at Stanford University, where he also serves as the Associate Chief Information Officer for Data Science at Stanford Health Care. His research focuses on integrating machine learning with medical ontologies to create a learning health system that enhances clinical decision-making and patient care. Dr. Shah has been recognized for his contributions to the field, being elected to the American College of Medical Informatics in 2015 and inducted into the American Society for Clinical Investigation in 2016. He holds an MBBS from Baroda Medical College in India, a PhD from Penn State University, and completed postdoctoral training at Stanford. With over 200 scientific publications and multiple patents to his name, Dr. Shah is at the forefront of advancing AI technologies in healthcare, striving to implement them ethically and effectively within clinical settings. His work not only addresses critical healthcare challenges but also aims to democratize medical knowledge through innovative data science applications.

Dr. Matthew Lungren is an Associate Professor of Medicine in the Department of Radiology at Stanford University and Co-Director of the Stanford Center for Artificial Intelligence in Medicine and Imaging. His research, funded by the NIH and NSF, focuses on the application of artificial intelligence and deep learning in medical imaging, precision medicine, and predictive health outcomes. Dr. Lungren's impactful work has garnered attention from major media outlets such as NPR, Vice News, and Scientific American, and he is a sought-after speaker at national and international scientific conferences on AI in healthcare. With a strong educational background that includes an MD from the University of Michigan and a Master of Public Health from the University of North Carolina, he has significantly contributed to advancing AI technologies in clinical settings. Dr. Lungren is also recognized for his teaching efforts, including his popular course on AI in Healthcare on Coursera, which aims to make complex topics accessible to a broader audience. Through his innovative research and commitment to education, Dr. Lungren continues to shape the future of healthcare through artificial intelligence.

Dr. Serena Yeung is an Assistant Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. She also serves as the Associate Director of Data Science for the Center for Artificial Intelligence in Medicine & Imaging and is affiliated with Stanford’s Clinical Excellence Research Center. Dr. Yeung's research focuses on developing advanced computer vision, machine learning, and deep learning techniques to interpret a wide range of visual data, including medical images, video captures of human behavior, and cell microscopy images. Her commitment to education is reflected in her graduate-level lectures on deep learning in computer vision, which have garnered over a million views online. Before her current role, she was a Technology for Equitable and Accessible Medicine (TEAM) Fellow at Harvard University. Dr. Yeung holds a PhD from Stanford University and has contributed significantly to the field through her leadership of the Medical AI and Computer Vision Lab at Stanford, as well as her participation on the NIH Advisory Committee to the Director Working Group on Artificial Intelligence.

Dr. Tina Hernandez-Boussard is an Associate Professor of Medicine (Biomedical Informatics), Biomedical Data Sciences, and Surgery at Stanford University. With a robust background in bioinformatics and health services research, her work focuses on applying innovative methods to large clinical datasets to enhance healthcare delivery. Over the past decade, Dr. Hernandez-Boussard has utilized electronic medical records and extensive digital data to monitor, measure, and predict healthcare outcomes through advanced machine learning and deep learning techniques. Her research infrastructure captures diverse data sources, transforming them into actionable knowledge that integrates seamlessly into clinical workflows. Dr. Hernandez-Boussard also serves as the Chairperson of the National Advisory Council for the Agency for Healthcare Research and Quality and is the Director of Faculty Development in Biomedical Informatics at Stanford. She holds a PhD in Computational Biology from Lyon University, a Master’s in Public Health from Yale University, and a Master’s in Health Services Research from Stanford University. Through her pioneering research and leadership roles, she is dedicated to advancing equitable healthcare practices and improving patient outcomes across diverse populations.