Shaping the Responsible Adoption of AI in Healthcare

Course overview

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 specialties; including pathology, cardiology, internal medicine, surgery, psychiatry and oncology. learning Objectives: 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