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Expert-Guided Large Language Models for Clinical Decision Support in Precision Oncology.

Jacqueline Lammert1,2,3,4, Tobias Dreyer1,3, Sonja Mathes5,6

  • 1Department of Gynecology and Center for Hereditary Breast and Ovarian Cancer, Technical University of Munich (TUM), School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital, Munich, Germany.

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Summary
This summary is machine-generated.

Medical Evidence Retrieval and Data Integration for Tailored Healthcare (MEREDITH) enhances large language model (LLM) capabilities for precision oncology. Integrating expert feedback and domain-specific data significantly improved MEREDITH's treatment recommendation accuracy and breadth.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • The rapid growth of medical literature presents challenges for oncologists seeking targeted therapies.
  • General-purpose large language models (LLMs) have limited clinical utility due to a lack of domain-specific knowledge.

Purpose of the Study:

  • To introduce MEREDITH, an LLM system designed to support treatment recommendations in precision oncology.
  • To leverage Google's Gemini Pro LLM with retrieval-augmented generation and chain-of-thought methodologies.

Main Methods:

  • MEREDITH was evaluated on 10 fictional oncology cases with iterative feedback from a molecular tumor board (MTB).
  • The system was enhanced to include clinical studies, trial databases, drug approval status, and oncologic guidelines.
  • Qualitative and quantitative assessments were performed, including measuring semantic cosine similarity between LLM suggestions and clinician responses.

Main Results:

  • MEREDITH identified a median of 4 treatment options, compared to 2 by MTB experts, including therapies based on preclinical data and combination treatments.
  • Incorporating a curated medical dataset contextualizing molecular targetability broadened treatment possibilities.
  • High concordance (94.7%) between MEREDITH and expert recommendations was achieved, with a significant increase in semantic similarity (0.71 to 0.76, P = .01).

Conclusions:

  • Expert feedback and domain-specific data are crucial for augmenting LLM performance in oncology.
  • Further research is needed to explore the responsible integration of LLMs into clinical workflows.