NLM DIR Seminar Schedule

Abstract:

Evidence-based medicine (EBM) is a clinical approach that prioritizes the integration of the best available evidence from well-designed research into decision-making for individual patient care. Despite its transformative potential, EBM faces significant barriers in both the generation and utilization of evidence. Evidence generation primarily relies on clinical trials, yet one of the major challenges to their success is patient recruitment. To address this, we introduced TrialGPT, an end-to-end framework leveraging large language models (LLMs) for zero-shot patient-to-trial matching. Similarly, LLMs also hold significant promise in facilitating the utilization of medical evidence. However, a critical limitation is their tendency for hallucination—producing plausible but factually incorrect content. To mitigate this issue, I will present our work on augmenting LLMs with domain-specific literature retrieval and database utilities. By grounding their outputs in high-quality, well-curated data, this approach substantially reduces the risk of hallucination and ensures that their generated content is based on solid medical evidence.