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Updated: Jan 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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

Yaoqian Sun¹, Zikang Chen¹, Hailing Cai¹

¹College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, 310027 Hangzhou, Zhejiang Province, China.

International Journal of Medical Informatics

|November 6, 2025

Summary

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 research Expert-in-the-loop Large language models Study 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.