NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Feb. 18, 2025 Samuel Lee
Efficient predictions of alternative protein conformations by AlphaFold2-based sequence association -
Feb. 25, 2025 Zhizheng Wang
GeneAgent: Self-verification Language Agent for Gene Set Analysis using Domain Databases -
March 4, 2025 Sofya Garushyants
TBD -
March 11, 2025 Sanasar Babajanyan
TBD -
March 18, 2025 MG Hirsch
TBD
RECENT SEMINARS
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Feb. 11, 2025 Po-Ting Lai
Enhancing Biomedical Relation Extraction with Directionality -
Feb. 4, 2025 Victor Tobiasson
On the dominance of Asgard contributions to Eukaryogenesis -
Jan. 28, 2025 Kaleb Abram
Leveraging metagenomics to investigate the co-occurrence of virome and defensome elements at large scale -
Jan. 21, 2025 Qiao Jin
Artificial Intelligence for Evidence-based Medicine -
Jan. 17, 2025 Xuegong Zhang
Using Large Cellular Models to Understand Cell Transcriptomics Language
Scheduled Seminars on Jan. 21, 2025
In-person: Building 38A/B2N14 NCBI Library or Zoom
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
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.