Shaping the Responsible Adoption of AI in Healthcare

 

As the use of artificial intelligence (AI) moves from being a curiosity to a necessity, it is clear that the benefit obtained from using AI models to prioritize care interventions is an interplay of the model’s performance, the capacity to intervene, and the benefit/harm profile of the intervention. We will begin the conversation reviewing the necessary data strategy to enable organization wide AI adoption and leading into a discussion of the core intuition behind foundation models. After a brief review of the kinds of use-cases that AI can serve across multiple medical specialties, we will discuss Stanford Healthcare’s efforts to shape the adoption of health AI tools to be useful, reliable, and fair so that they lead to cost-effective solutions that meet health care's needs. The conversation will draw on examples from multiple specialities including pathology, cardiology, internal medicine, surgery, psychiatry and oncology.

Originally published on August 6, 2024

Lecture Presenter

Nigam H. Shah, MBBS, PhD

Nigam H. Shah, MBBS, PhD

Professor of Medicine and of Biomedical Data Science, Associate Dean for Research
Stanford School of Medicine
Associate Director, Stanford Center for Biomedical Informatics Research
Stanford University
Chief Data Scientist
Stanford Healthcare

Dr. Nigam Shah is mrofessor of Medicine at Stanford University, and chief data scientist for Stanford Healthcare. His research group analyzes multiple types of health data (EHR, claims, wearables, weblogs, and patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system. At Stanford Healthcare, he leads artificial intelligence and data science efforts for advancing the scientific understanding of disease, improving the practice of clinical medicine and orchestrating the delivery of healthcare. Dr. Shah is an inventor on eight patents and patent applications, has authored over 200 scientific publications and has co-founded three companies. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and was 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.

Objectives

After this presentation, participants will be able to:

  • Describe the importance of the interplay of a model's output, the intervention policy, and work capacity in making AI useful
  • Summarize the need of a data strategy to underpin AI adoption
  • Describe the different kinds of roles models can play in healthcare

Sponsored by:

University of Utah School of Medicine, Department of Pathology, and ARUP Laboratories