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Responsible AI in a Healthcare System

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 prioritise care interventions is an interplay of the model’s performance, the capacity to intervene, and the benefit/harm profile of the intervention. 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 healthcare’s needs. 

We will conclude with the rationale and vision for collaborative activities such as the Coalition for Health AI. We will discuss how the adoption of LLMs in medicine needs to be shaped by performing the evaluations that specify the desired benefits and verify those benefits via testing in real-world deployments. The conversation will draw on examples from multiple specialties including pathology, cardiology, internal medicine, surgery, psychiatry and oncology.

In this forum we will be joined by Dr Nigam Shah!

Dr Nigam Shahhas Shah has been Chief Data Scientist at Stanford Healthcare since 2022. 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. His research group analyses 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.

This Community Forum will be taking place virtually on 18 February at 16:00-17:30 (GMT) / 11:00-12:30 (EST) / 17:00-18:30 (CET)