Bringing AI into Healthcare Safely and Ethically

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Artificial intelligence has the potential to transform healthcare, driving innovations, efficiencies, and improvements in patient care. But, this powerful technology also comes with a unique set of ethical and safety challenges. So, how can AI be integrated into healthcare in a way that maximizes its potential while also protecting patient safety and privacy? 

In this session faculty from the Stanford AI in Healthcare specialization will discuss the challenges and opportunities involved in bringing AI into the clinic, safely and ethically, as well as its impact on the doctor-patient relationship. They will also outline a framework for analyzing the utility of machine learning models in healthcare and will describe how the US healthcare system impacts strategies for acquiring data to power machine learning algorithms.

You Will Learn:
  • How to evaluate the value of using artificial intelligence and machine learning to improve healthcare
  • How to identify and reconcile ethical considerations when implementing algorithmic solutions in healthcare
  • How incentives inherent in the healthcare ecosystem affect the approach to creating a machine learning healthcare system
Presented By
Stanford Artificial Intelligence in Healthcare Specialization

Contact us at [email protected] or 650-204-3984

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Tina Hernandez-Boussard

Laurence Baker

Dr. Baker is Bing Professor of Human Biology and Professor in the Health Policy group at Stanford University. He is a health economist who applies economic and statistical analysis to study challenges facing the healthcare system. Professor Baker teaches undergraduate and graduate courses at Stanford, and has published widely on a range of healthcare system and financing issues. Professor Baker also holds appointments as Professor of Economics (by courtesy) at Stanford, Senior Fellow of the Stanford Institute for Economic Policy Research, and Research Associate of the National Bureau of Economic Research in Cambridge, MA. He is a former Chair of the Department of Health Research and Policy at Stanford.

Tina Hernandez-Boussard

Dr. Hernandez-Boussard is Associate Professor at Stanford University in Medicine (Biomedical Informatics), Biomedical Data Sciences, Surgery and Epidemiology & Population Health (by courtesy). Her current work utilizes high-volume digital data to monitor, measure, and predict healthcare outcomes using natural language processing and machine/deep learning techniques to analyze both structured and unstructured data. Through this infrastructure, her team captures heterogenous data sources, transforms these diverse data to knowledge, and uses this knowledge to improve patient care and outcomes.

Matthew Lungren

Dr. Lungren is Co-director of the Stanford Center for Artificial Intelligence in Medicine and Imaging, and Medical School Faculty in the Department of Radiology at Stanford University Medical Center. Dr. Lungren’s NIH- and NSF-funded research is in the field of AI and deep learning in medical imaging, precision medicine, and predictive health outcomes. His work has been featured in national news outlets such as NPR, Vice News, Scientific American, and he regularly speaks at national and international scientific meetings on the topic of AI in healthcare.

Nigam Shah

Dr. 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 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.

Serena Yeung

Dr. Yeung is Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering at Stanford University. She is affiliated with Stanford’s Clinical Excellence Research Center, and serves as Associate Director of Data Science for the Center for Artificial Intelligence in Medicine & Imaging. Dr. Yeung’s research focuses on computer vision, machine learning, and deep learning for interpreting diverse types of visual data. She has also served on the National Institute of Health's Advisory Committee to the Director Working Group on Artificial Intelligence.