François Grolleau
This talk examines two critical aspects of data-driven medicine: personalized treatment strategies using causal inference and the robust evaluation of large language models (LLMs) for clinical applications.
In the first part, we present a novel approach to personalized medicine, applying causal statistical learning to observational data to develop individualized treatment rules. We focus on optimizing the timing of renal replacement therapy initiation in acute kidney injury, demonstrating: (i) the estimation and validation of an optimal dynamic strategy, and (ii) a comprehensive framework for evaluating individualized rules using observational data.
In the second part, we tackle the challenge of evaluating LLMs in medicine, focusing on the generation of hospital course summaries. Current evaluation methods are often either unscalable (physician-led) or untrustworthy for clinical settings (LLM-as-a-judge). We propose a rubric-based approach to LLM evaluation that combines the scalability of automated methods with the trustworthiness demanded by medical applications, paving the way for responsible deployment of LLMs in healthcare.