AI + Health 2022 On-Demand

Join us for the opening session with welcome remarks from Curt Langlotz, the Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), and two plenary talks from Dr. John Halamka, President of the Mayo Clinic Platform, and Pricilla Chan, Co-Founder and Co-CEO of the Chan Zuckerberg Initiative.

Curt Langlotz
Director, Center for Artificial Intelligence in Medicine and Imaging, Stanford University

John Robichaux
Director of Education, Stanford University, Institute for Human-Centered AI (HAI)

John D. Halamka
President, Mayo Clinic

Natalie Pageler
Chief Medical Information Officer, Stanford Medicine Children's Health

Priscilla Chan Zuckerberg
Co-Founder and Co-CEO, Chan Zuckerberg Initiative

Artificial intelligence in oncology is a rapidly growing and evolving field with the development of many new algorithms over the last two years. In this session, we will discuss the current landscape of AI in oncology, potential pitfalls and regulatory challenges, and the opportunities for the future with leading academic and industry leaders working in this space.

Joseph Liao
Professor of Urology, Stanford University

Lei Xing
Jacob Haimson & Sarah S. Donaldson Professor, Stanford University

Mohsen Bayati
Professor, Stanford University

Ruijang Li
Associate Professor of Radiation Oncology, Stanford University

Advancements of machine learning and artificial intelligence into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. In this session, radiologists discuss current areas of impact of AI in medical imaging and what the future might hold.

Ali Syed
Clinical Assistant Professor, Pediatric Radiology, Stanford University 

Evan Zucker
Stanford University

Sergios Gatidis
Machine Learning Scientist, University Hospital Tübingen

Shreyas Vasanawala
William R. Brody Professor of Pediatric Radiology and Child Health, Stanford University

What does AI-Driven Diagnostics look like today, and how will it transform in the future? This session reviews different AI-based tools for use in the clinic and hospital that support clinical diagnosis. The session starts with four lightning talks from presenters in academia and industry followed by a discussion from our panelists.

Daniel Kim
Assistant Professor / Associate Member, UC Santa Cruz / Stanford Canary Center

Edward Chang
Adjunct Professor, Computer Science, Stanford University

Olivier Gevaert
Associate Professor of Medicine and of Biomedical Data Science, Stanford University

Despite being the largest U.S. health care delivery platform, primary care is at risk of being left behind in the health care AI revolution. Primary care investigators from leading U.S. family medicine departments and winners of the 2022 American Board of Family Medicine (ABFM) AI Grants present what they’re doing to advance a shared vision to strengthen the foundation of U.S. health systems – with AI designed for and by primary care.

Ammar Mandvi
Assistant Clinical Professor of Family Medicine & Clinical Informaticist, UC San Diego

Carlos Roberto Jaen
Professor and Chair of Family and Community Medicine, UT Health San Antonio

Gene Kallenberg
Professor, Family and Preventative Medicine, UCSD School of Medicine

John Maier
Assistant Professor of Family Medicine, University of Pittsburg

LeChauncy Woodard
Director of the Humana Integrated Health System Sciences Institute, University of Houston

Robert Phillips
Founding Executive Director, The Center For Professionalism & Value In Health Care, American Board Of Family
Medicine Foundation

Steven Lin
Executive Director, Stanford Healthcare AI Applied Research Team

Winston Liaw
Chair, Health Systems and Population Health Sciences, University of Houston College of Medicine

Yun Shi
Clinical Assistant Professor, Department of Family and Community Medicine, University of Texas Health San Antonio

Today’s most innovative startups and effective healthcare systems are increasingly driven by data and artificial intelligence (AI). Understanding the value of data and AI is a critical differentiator for healthcare professionals today. In this session, panelists take participants through the design and build process of AI-ready data infrastructures. Participants explore the latest developments, challenges and solutions in data and AI valuation, the key technologies in play, questions around data markets, as well as the implications of regulatory, privacy and fairness considerations of these new platforms.

David Ouyang
Staff Physician, Cedars Sinai

Kristen Yeom
Professor of Radiology, Neurology and Neurosurgery, Stanford University

Louise Sun
Professor and Chief, Division of Cardiac Anesthesiology, Stanford University School of Medicine

Michael Mathis
Assistant Professor of Anesthesiology, University of Michigan Medicine

Edward Lee
Post Doc Fellow, Stanford University

The capability of AI continues to grow as cutting-edge research continues to make advances. What does this mean for Digital Health? This session explores the possibilities of integrating AI with digitally enabled care models. We discuss the opportunity for AI-powered innovations in healthcare and how business should think of strategic implementations of these innovations. Thought leaders in AI, medical informatics, and digital health discuss what the future of digital health looks like.

Chris O'Dell
Executive Director, Digital Health Care Integration, Stanford Health Care

Justin Norden
Partner, GSR Ventures

Lisa Gohil
Director, Digital Health Strategy, Stanford Health Care

Parker Holcomb
Chief AI Engineer, Elevance Health

Rebecca Mitchell
Advisor, Investement Team, Vive Collective

Ron Li
Medical Informatics Director, Digital Health, Stanford Medicine Health Care

In this session, we hear from a panel of clinical, academic and industry experts who discuss the greatest needs, challenges and opportunities for AI in pathology. Discussion topics include current clinical approaches and impact, latest promising developments, clinical quality control, and downstream and societal impacts.

Ehsan Adeli
Assistant Professor, Psychiatry and Behavioral Sciences, Stanford University

Jonathan Long
Assistant Professor of Pathology, Stanford University

Magdalini Paschali
Postdoctoral Scholar, Stanford University, Computational Neuroimage Science Laboratory

Renee Yao
Global Healthcare AI Startups Business Development Lead, NVIDIA

Sharmila Majumdar
Margaret Hart Surbeck Distinguished Professor in Advanced Imaging & Vice Chair for Research, UCSF

An engaging session discussing current and future trends for AI in precision health and medicine with three presentations discussing how AI is being used in this emerging field. An AI-driven Taxonomy for Prematurity by Nima Aghaeepour, PhD. Using Medical Imaging and AI for Precision Health in Population Studies by Akshay Chaudhari, PhD, and using Real-World Data and AI to Advance Clinical Trials and Precision Oncology by James Zou, PhD

Akshay Chaudhari
Assistant Professor of Radiology and, by courtesy, of Biomedical Data Science, Stanford University

James Zou
Assistant Professor, Stanford University

Nima Aghaeepour
Associate Professor, Stanford University

A recent National Academy of Medicine paper acknowledged that AI-based diagnostic decision support tools have broad potential to revolutionize the field of medical diagnostics; however these AI tools may fail to achieve wide uptake if there is insufficient clinical acceptance. The authors present a framework for considering and overcoming barriers to adoption centered around the reason, means, method, and desire to use these tools while addressing intersecting issues of equity.

Amelia Sattler
Clinical Assistant Professor of Medicine, Primary Care and Population Health, Stanford University

Andrew Gonzalez
Assistant Professor of Surgery; Associate Director for Data Science, Indiana University School of Medicine; Regenstrief Institute

Barbara Evans
Professor of Law and Stephen C. O’Connell Chair; Professor of Engineering

Cornelius James
Clinical Assistant Professor, Internal Medicine, Pediatrics and Learning Health Sciences, University of Michigan

Mark Sendak
Population Health & Data Science Lead, Duke Institute for Health Innovation

Steven Lin
Executive Director, Stanford Healthcare AI Applied Research Team

Despite significant advancements in the science of healthcare AI, research and development of AI tools impacting health seldom include the perspectives and values of patients, diverse communities, and the general public. Through two case studies, experts from academia, industry, public health and the safety net community examine the critical role of community engaged research and public data in making healthcare AI more just, equitable, inclusive, and useful.

Carol McCall
Chief Health Analytics Officer

Dale Webster
Director of Research, Health AI, Google

Drew Bedgood
Research Data Analyst; Clinical Informaticist, California Department of Public Health

Justin Ko
Clinical Assistant Professor, Dermatology, Stanford University

Lee Sanders
Professor of Pediatrics and, by courtesy, of Epidemiology and Population Health, Stanford University

Mike Gonzalez
Director of Community Engagement, Santa Clara Family Health Plan

Ria Paul
Clinical Assistant Professor, Medicine, Stanford Medicine

Shreya Shah
Clinical Assistant Professor, Medicine, Stanford University School of Medicine

Steven Lin
Executive Director, Stanford Healthcare AI Applied Research Team