Leveraging Large Datasets and Large Language Models to Improve Health Equity

Abstract:
The proliferation of medical data and the advancements of large language models (LLMs) promise to revolutionize healthcare; however, ensuring and increasing health equity remains a significant challenge. In this talk, I will present recent work on two critical aspects of this evolving landscape. First, I will examine the unexpected consequences of multi-source data scaling. Counter to intuition, adding training data can sometimes reduce overall accuracy, produce uncertain fairness outcomes, and diminish worst-subgroup performance. These findings underscore the complexity of working with disparate data sources in healthcare AI. Next, I will showcase innovative applications of LLMs in women's health. Through participatory design with healthcare workers and patients, we've developed guiding principles for LLM use in maternal health. Additionally, we demonstrate how LLMs can generate rationales for contraceptive medication switches using clinical notes. The talk concludes by emphasizing vigilance and ethical considerations as we advance towards more data-driven and AI-assisted healthcare.