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Learning from experts: A self-improving LLM framework for study population generation in clinical research.

Yaoqian Sun1, Zikang Chen1, Hailing Cai1

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Summary
This summary is machine-generated.

CriteriaLLM, a novel framework, uses large language models (LLMs) with clinician feedback to generate credible study populations from clinical objectives. This expert-in-the-loop approach enhances real-world evidence generation for clinical research.

Keywords:
Clinical researchExpert-in-the-loopLarge language modelsStudy population

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Area of Science:

  • Artificial Intelligence in Clinical Research
  • Real-World Data (RWD) and Real-World Evidence (RWE) Generation
  • Clinical Study Design Optimization

Background:

  • Electronic health records have increased real-world data (RWD) availability for real-world evidence (RWE) generation.
  • Large language models (LLMs) aid RWD research but struggle with interpretable and credible study population design.
  • Bridging study objectives and downstream analyses for RWD research remains a challenge.

Purpose of the Study:

  • To introduce CriteriaLLM, a framework enabling LLMs to generate eligible study populations from clinical research objectives.
  • To incorporate clinician feedback and an expert knowledge base for enhanced LLM-driven population design.
  • To improve the interpretability and credibility of LLM-generated study populations.

Main Methods:

  • CriteriaLLM integrates clinician feedback into LLMs for study population generation.
  • An expert knowledge base, inspired by after-action reviews, records LLM outputs and clinician modifications.
  • A dual-retrieval algorithm (disease domain relevance and lexical similarity) guides LLM generations using historical cases.
  • A continuous validation loop with iterative expert feedback refines model performance.

Main Results:

  • The framework was evaluated on 254 published clinical studies using MIMIC-III data and four LLMs (GPT-4o, Deepseek-R1, LLaMA models).
  • CriteriaLLM achieved a high Macro F1 score of 0.9180 in generating quality study populations.
  • The framework demonstrated generalizability across LLMs with varying sizes and deployment methods.

Conclusions:

  • CriteriaLLM enables LLMs to generate eligible study populations from clinical objectives without fine-tuning.
  • Structured expert feedback and retrieval guidance enhance the quality and reliability of study criteria.
  • The framework offers a scalable, self-improving approach for integrating AI into clinical research, ensuring clinical appropriateness and interpretability.