health AI hub
Health AI Hub Collaboratory Rounds bring together experts and practitioners to share and explore various aspects of AI lifecycle management in healthcare. These monthly gatherings are designed to keep you informed about the latest trends, best practices, and challenges in the dynamic field of healthcare AI.
Date and Time
May 28 at 3pm ET / 12pm PT
Topic
The Landscape of AI Implementation in US Hospitals: An Emerging Divide in Adoption and Benefit
Session Description
Artificial intelligence (AI) has the potential to improve healthcare delivery, but uneven adoption and implementation can reinforce existing care gaps and inefficiencies.
In this session, the authors of “The landscape of AI implementation in US hospitals“ share findings from their recent study, analyzing data from over 3,500 hospitals to examine where AI is implemented, what factors are associated with implementation, and the patterns of early AI adoption across geographical regions.
The findings highlight a critical paradox: while AI holds promise for improving care, its implementation is clustered in specific regions, leaving areas with the greatest healthcare needs less likely to benefit. The authors will explore “hotspots” and “coldspots” of AI adoption, unpack the regional factors driving these disparities, and explain how local context influences whether hospitals implement predictive AI models.
Attendees will gain insight into the current landscape of AI in healthcare, why a one-size-fits-all approach to deployment may fall short, and what steps are needed to ensure more equitable and effective implementation moving forward.
After registering, you will receive a confirmation email with the link to join the meeting.
Speakers
Tina Hernandez-Boussard, PhD MPH, MS, FACMI
Dr. Hernandez-Boussard is an Associate Dean of Research and Professor of Medicine (Biomedical Informatics), Biomedical Data Sciences, Surgery and Epidemiology & Population Health (by courtesy) at Stanford University. Her work is at the intersection of informatics and population health, promoting responsible AI across populations. She utilizes diverse, multimodal data to develop rigorous criteria and guidelines that steer the development of responsible AI, aiming to bridge gaps in health care and enhance patient outcomes. Dr. Hernandez-Boussard advocates for practices that ensure the benefits of digital technologies are realized across all segments of society.
Madelena Ng, DrPH, MPH
Dr. Madelena Ng is a Postdoctoral Scholar in the Stanford Center for Biomedical Ethics (SCBE), training under the NHGRI T32 Ethical, Legal and Social Implications (ELSI) Research Program. Dr. Ng is an applied health scientist whose work evaluates the real-world impact of emerging technologies on people and society. As generative AI continues to advance rapidly, she aims to embed ethics into the core of health AI development and operations. Alongside Dr. Tina Hernandez-Boussard, she has led foundational work in AI ethics, governance, and responsible innovation. Dr. Ng’s research has consistently demonstrated that technological solutions alone are insufficient in addressing the shortcomings inherent in the biomedical research ecosystem. This challenge is similarly reflected in health AI, where limited generalizability and inequitable access across populations remain pressing concerns.
Yeon-Mi Hwang, PhD
Yeon-Mi Hwang is a Postdoctoral Scholar at Stanford Medicine in the Division of Computational Medicine. Her research sits at the intersection of biomedical informatics and health equity, with a focus on understanding who benefits from advances in health AI and who gets left behind. She takes a multi-level approach, spanning modeling comorbidity and disease burden at the individual level, examining disparities in vulnerable populations, and studying patterns of AI adoption across healthcare systems. She is the first author of The landscape of AI implementation in US hospitals, which examines geographic disparities in clinical AI adoption across U.S. hospitals.
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The Future of AI Clinician Copilots
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Encoding Bioethics: AI in Clinical Decision Making
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