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Updated: Sep 17, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Generative-AI-Based Approaches for Information Extraction from Clinical Notes: A Scoping Review.

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Studies in Health Technology and Informatics
|July 1, 2025
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Large Language Models (LLMs) show promise for clinical data extraction, with GPT-4 excelling. Further research is needed to address reliability and privacy concerns before widespread clinical use.

Keywords:
Information ExtractionLarge Language ModelsPrompt Engineering

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Medicine
  • Natural Language Processing

Background:

  • Large Language Models (LLMs) are increasingly adopted in healthcare.
  • Challenges include reliability, hallucinations, and data privacy in clinical document analysis.

Purpose of the Study:

  • To evaluate the efficacy of LLMs in extracting structured data from clinical documents.
  • To assess prompt engineering strategies and model performance in this domain.

Main Methods:

  • Scoping review of 16 studies published between 2019 and 2025.
  • Focused on LLM performance in extracting data from clinical practice guidelines and notes.

Main Results:

  • GPT-4 demonstrated superior performance, leading in 11 out of 16 studies.
  • Achieved >85% accuracy/F1-score for entity extraction in multiple studies.
  • Performance varied across different clinical document types.

Conclusions:

  • LLMs, particularly GPT-4, show significant potential for clinical data extraction.
  • Variability in performance and data privacy necessitate further research and ethical considerations for deployment.