Title: "Shaping the Responsible Adoption of AI in Healthcare" Featuring Nigam Shah, MBBS, PhD, Professor of Medicine at Stanford University and
Chief Data Scientist at Stanford Health Care.
This DBMI Grand Rounds was presented on Wednesday, April 3, 2024, at 12:00 pm CT. Visit here for more VUMC DBMI events: www.vumc.org/d...
Abstract:
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. We will conclude with the rationale and vision for collaborative activities such as the Coalition for Health AI.
Learning Objectives:
1. Be able to articulate the importance of the interplay of a model's output, the intervention policy, and work capacity in making AI useful.
2. Be able to summarize the need of a data strategy to underpin AI adoption.
3. Be able to describe the different kinds of roles models can play in healthcare.
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