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Assessing the Accuracy of Large Language Models on European Guidelines for Cervical Cancer: An In Silico Benchmarking

Matteo Pavone1,2,3,4, Chiara Innocenzi1,2, Nicola Macellari1

  • 1UOC Ginecologia Oncologica, Dipartimento di Scienze per la Salute Della Donna e del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.

BJOG : an International Journal of Obstetrics and Gynaecology
|November 25, 2025
PubMed
Summary

Large language models show varying accuracy in answering cervical cancer questions. ChatGPT 4.0 performed best, but expert review is crucial for safe clinical use.

Keywords:
artificial intelligencecervical cancerchatgptdeepseekdigital surgerygeminilarge language models

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Guidelines
  • Oncology

Background:

  • Large language models (LLMs) are increasingly adopted in healthcare, yet their information validity requires rigorous assessment.
  • The accuracy of LLM-generated medical information is critical for clinical and research applications.

Purpose of the Study:

  • To evaluate the accuracy, consistency, and reliability of ChatGPT 4.0, DeepSeek R1, and Gemini 2.0.
  • To assess LLM performance in answering cervical cancer questions based on European Society of Gynaecologic Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology (ESGO/ESTRO/ESP) guidelines.

Main Methods:

  • A prospective, comparative in silico benchmarking study.
  • Fifty cervical cancer guideline-derived questions were posed to three LLMs (ChatGPT 4.0, DeepSeek R1, Gemini 2.0).
  • Answers were evaluated for accuracy (Global Quality Score), consistency, and reliability against ESGO/ESTRO/ESP guidelines.

Main Results:

  • ChatGPT 4.0 demonstrated superior performance with 42% of responses rated GQS 5, compared to Gemini 2.0 (30%) and DeepSeek R1 (28%).
  • ChatGPT 4.0 achieved a higher median GQS (4.00) than DeepSeek R1 and Gemini 2.0 (3.50).
  • Response consistency varied significantly between models, while reliability showed no significant differences.

Conclusions:

  • All evaluated LLMs exhibited suboptimal accuracy in adhering to clinical guidelines for cervical cancer.
  • ChatGPT 4.0 was the most accurate and consistent model, whereas DeepSeek R1 underperformed.
  • Despite comparable reliability, expert oversight is essential for safe clinical integration of LLMs to prevent misinformation.