# Operationalizing Large Language Models for Clinical Research Data Extraction: Methods, Quality Control, and Governance

## TL;DR

This paper reviews how large language models can be used for clinical data extraction, focusing on methods, quality control, and governance for real-world applications.

## Contribution

The paper introduces a multidimensional evaluation framework and an operational governance checklist for deploying LLMs in clinical research.

## Key findings

- LLMs show improvements in clinical data extraction tasks like diagnosis and medication records.
- Key challenges include domain shift, hallucinations, privacy constraints, and cost/latency trade-offs.
- Future directions involve multimodal extensions and human–machine collaboration for better precision medicine.

## Abstract

Methods

This narrative review drew on targeted searches of PubMed/MEDLINE and arXiv (January 2020–October 2025), verification of peer-reviewed versions via ACL Anthology for selected preprints, and citation tracking of seminal literature. In this review, we trace the methodological evolution from rules to encoder-based models and LLMs, propose a multidimensional evaluation framework for real-world deployment—which includes accuracy, structural quality, human-in-the-loop effort, stability, and compliance—and develop an operational governance checklist to support auditable and reproducible implementations. Using representative tasks—diagnosis extraction, medication records, clinical trial data, and phenotype integration—we summarize the improvements and failure modes of LLM-based extraction and analyze key challenges, including domain shift, factual “hallucinations,” privacy and regulatory constraints, and cost/latency trade-offs. Finally, we outline future directions through which multimodal and cross-lingual extensions, human–machine collaborative annotation, and standardized reporting practices can advance precision medicine and sustainable, high-quality clinical research.

The online version contains supplementary material available at 10.1007/s10916-026-02353-w.