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

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Large Language Model Versus Manual Review for Clinical Data Curation in Breast Cancer: Retrospective Comparative

Young-Joon Kang¹, Hocheol Lee², Jae Pak Yi¹

¹Department of Surgery, College of Medicine, The Catholic University of Korea, Incheon St Mary's Hospital, 56, Dongsu-ro, Bupyeong-gu, Incheon, 21431, Republic of Korea, 01026383847.

JMIR Medical Informatics

|November 6, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) achieved 90.8% accuracy in extracting breast cancer data, significantly reducing processing time and physician hours compared to manual review. This automated approach enhances efficiency in clinical research.

Keywords:

artificial intelligence breast neoplasms clinical oncology data mining large language model natural language processing

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Published on: May 17, 2019

Area of Science:

Oncology
Medical Informatics
Artificial Intelligence

Background:

Manual review of electronic health records (EHRs) for clinical research is time-consuming and inconsistent.
Large language models (LLMs) show promise for automating data extraction, but their use in surgical oncology is not well-studied.

Purpose of the Study:

To assess the feasibility and accuracy of LLM-based data processing versus manual physician review for extracting clinical information from breast cancer patient records.

Main Methods:

A retrospective comparative study analyzed breast cancer records from five academic hospitals.
Two pathways were compared: manual physician review and LLM (Claude 3.5 Sonnet) processing of deidentified data.
Validation involved 900 data points per group, assessed by four breast surgical oncologists, with accuracy compared to national registry data.

Main Results:

The LLM achieved 90.8% accuracy, comparable to manual review.
LLM processing reduced physician hours by 91% (12 days vs. 7 months) and captured more survival events.
While LLM showed better lymph node documentation, manual review had less missing cancer staging data.

Conclusions:

LLM-based curation of automatically extracted, deidentified data is effective and significantly more efficient than manual review.
This two-step approach balances data privacy and research efficiency.
LLM processing accelerates retrospective clinical research while maintaining data quality and patient privacy.