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  11. Development Of A Synthetic Oncology Pathology Dataset For Large Language Model Evaluation In Medical Text Classification.
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  11. Development Of A Synthetic Oncology Pathology Dataset For Large Language Model Evaluation In Medical Text Classification.

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Development of a Synthetic Oncology Pathology Dataset for Large Language Model Evaluation in Medical Text Classification.

Werner O Hackl1, Sabrina B Neururer1,2, Stefan Richter1,3

  • 1Division for Digital Health and Telemedicine, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria.

Studies in Health Technology and Informatics
|April 24, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A synthetic oncology pathology dataset was created for evaluating Large Language Models (LLMs) in cancer report classification. This privacy-preserving benchmark enables reproducible AI research without using real patient data.

Keywords:
Large Language ModelsNeoplasm RegistriesText Mining

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Area of Science:

  • Artificial Intelligence in Pathology
  • Computational Pathology
  • Medical Informatics

Background:

  • Large Language Models (LLMs) show potential for automating oncology pathology report classification.
  • Real patient data use is limited by privacy, legal, and ethical concerns.
  • Privacy-compliant alternatives are crucial for AI research in this field.

Purpose of the Study:

  • To develop a synthetic oncology pathology dataset.
  • To establish a benchmark for evaluating LLM performance in pathology report classification.
  • To facilitate reproducible and privacy-preserving AI research.

Main Methods:

  • Generated 227 synthetic pathology reports using diverse LLMs (Microsoft Copilot, ChatGPT Plus, Perplexity Pro).
  • Included prostate, lung, and breast cancer cases, balanced for malignant and benign findings.
  • Validated reports through classification by three independent cancer registrars.

    • Main Results:

    • Created a structured dataset of synthetic oncology pathology reports.
    • Ensured structural and linguistic diversity across generated reports.
    • Achieved consensus-based validation by expert registrars.

    Conclusions:

    • The synthetic dataset serves as a clinically relevant benchmark for LLM evaluation.
    • Enables AI model assessment in pathology text classification without compromising patient privacy.
    • Supports scalable and ethical development of AI in oncology documentation.