Biostatistics Weekly Seminar

Can AI deliver on its promise for improving public health? Challenges in the clinical trial setting

Manisha Desai, PhD
Kim and Ping Li Professor of Medicine, Biomedical Data Science, and of Epidemiology and Population Health
Associate Dean of Research
Section Chief of Biostatistics
Director of the Quantitative Sciences Unit
Director of the Biostatistics Shared Resource for the Stanford Cancer Institute
Stanford University

Data science has played an essential role in solving many public health problems. For example, studies that leverage imaging data from the healthcare system have provided insight into how to more accurately stage cancer. Clinical trials are data-intensive and are the gold standard for establishing standard of care for treating many diseases. More recently there has been a rise in the use of data science to develop artificial intelligence (AI)-based tools that present promising solutions of how we diagnose, monitor, and treat patients. There are many challenges to consider including the underlying data used to establish AI-based algorithms, the way AI-based interventions are evaluated, and how the tools are deployed in practice. Ideas and potential solutions are illustrated using a real study.

Virtual: Zoom Link to Follow
31 January 2024

Speaker Itinerary

Topic revision: r1 - 19 Jan 2024, CierraStreeter

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