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Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using

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  • 1Department of Internal Medicine III, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Ismaninger Str. 22, Munich, Germany, 49 89-4140-8753.

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Large language models (LLMs) demonstrate high accuracy in extracting oncology data and performing survival analysis directly from clinical notes, enabling just-in-time (JIT) research. This study shows LLMs can bypass traditional methods for faster, more efficient oncology research.

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Area of Science:

  • Oncology research
  • Artificial intelligence in medicine
  • Clinical data analysis

Background:

  • Traditional cancer registries are labor-intensive and hinder timely research.
  • Large language models (LLMs) show promise for automating data extraction.
  • The potential of LLMs for direct, just-in-time (JIT) analysis of clinical narratives is underexplored.

Purpose of the Study:

  • To evaluate Gemini 2.5 Pro's ability to enable a JIT clinical oncology analysis paradigm.
  • Assess LLM performance in high-fidelity data extraction, complex clinical query answering, automated survival analysis, and hypothesis generation from free-text clinical data.

Main Methods:

  • Utilized a synthetic dataset of 240 unstructured clinical letters for stage IV non-small cell lung cancer (NSCLC).
  • Evaluated Gemini 2.5 Pro on four JIT capabilities: multiparameter extraction, direct query answering, survival analysis with code generation, and hypothesis generation.
  • Measured performance using accuracy, numerical deviation, log-rank P value, Harrell concordance index, and qualitative hypothesis evaluation.

Main Results:

  • LLM achieved >99% accuracy in data extraction, comparable to human extraction but significantly faster.
  • LLM answered complex queries with <1.5% deviation and autonomously performed end-to-end survival analysis, generating statistically indistinguishable Kaplan-Meier curves.
  • LLM generated biologically plausible and potentially novel hypotheses, with human-in-the-loop needed for AI-flagged ambiguities.

Conclusions:

  • Frontier LLMs like Gemini 2.5 Pro can perform high-fidelity data extraction, querying, and survival analysis directly from unstructured text.
  • This study provides a proof of concept for JIT clinical analysis, potentially revolutionizing oncology research.
  • Validation on real-world clinical data is crucial before clinical implementation.