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  3. Using Large Language Models To Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study.
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  3. Using Large Language Models To Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study.

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Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study.

Denise Lee1, Akhil Vaid2, Kartikeya M Menon1

  • 1Department of Surgery, Icahn School of Medicine at Mount Sinai, 10 Union Square East, Suite 2L, New York, NY, 10003, United States, 1 212 241 2891.

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|April 7, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A locally deployed large language model (LLM) demonstrated significant time savings for medical question answering (MQA) from surgical pathology reports, achieving 89% accuracy. Further optimization could enhance clinical data extraction.

Keywords:
NLPartificial intelligenceendocrine surgeryframeworklarge language modelmedicalmedical questionnatural language processingprivacyreportsurgical pathologythyroid cancer

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Area of Science:

  • Clinical Natural Language Processing (NLP)
  • Artificial Intelligence in Healthcare

Background:

  • Large language models (LLMs) offer potential for clinical NLP tasks like medical question answering (MQA).
  • Concerns regarding cost, computing power, and patient privacy limit LLM adoption in healthcare.
  • Open-source LLMs deployed within institutional firewalls can mitigate privacy risks.

Purpose of the Study:

  • To evaluate the performance of a locally deployed LLM for automated MQA from surgical pathology reports.
  • To compare the accuracy and efficiency of an LLM against human reviewers in extracting clinical information.

Main Methods:

  • 84 thyroid cancer surgical pathology reports were analyzed by two human reviewers and the FastChat-T5 LLM.
  • Reports were segmented, converted to embeddings, and contextually integrated for LLM processing.
  • Twelve medical questions were posed to extract staging and recurrence risk data, with response time and concordance evaluated.

    • Main Results:

    • Human reviewers achieved 99% concordance.
    • The LLM achieved 89% concordance with human reviewers.
    • The LLM responded significantly faster (19.56 minutes) compared to human reviewers (170.7 and 115 minutes).

    Conclusions:

    • Locally deployed LLMs offer substantial time savings for MQA in clinical settings with acceptable accuracy.
    • Prompt engineering and fine-tuning can further improve automated data extraction from clinical narratives.
    • LLMs hold promise for real-time clinical insights when privacy is ensured.