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Related Experiment Video

Updated: May 10, 2025

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

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

Werner O Hackl¹, Sabrina B Neururer^1,2, Stefan Richter^1,3

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

Summary

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 Models Neoplasm Registries Text 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.