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July 21, 2026Human Reproduction

Can AI speak endo? A multi-platform evaluation of large language models against ESHRE endometriosis guidelines

Gaetano Riemma Florindo Mario Caniglia Camilla Casolari Antonio Maiorana Mauro Cozzolino Vittorio Agrifoglio Pasquale De Franciscis Luigi Cobellis Raffaela Carotenuto Andrea Etrusco

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Key Points

This research aims to assess the performance of widely used large language models against the ESHRE endometriosis guidelines.
Cross-sectional, multi-platform comparative design evaluating three large language models.
Fifty questions from the 2022 ESHRE guideline submitted; assessed for accuracy and consistency using a zero-shot prompting approach.
Responses reviewed by independent experts using the Global Quality Score (GQS), consistent responses defined, and subgroup analyses conducted.
Model A achieved the highest accuracy (median GQS 5, IQR 4-5), significantly outperforming Model B (P < 0.001).
Model C showed superior consistency (92% reproducibility) compared to Models A (72%) and B (68%).
Overall reliability was similar across models with no significant differences, but all models showed improved GQS under API access.

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Implication

Cross-sectional study evaluates large language models' accuracy and reliability for endometriosis guidelines, indicating need for human oversight.

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Cite This Study

Riemma et al. (2026) studied this question.

synapsesocial.com/papers/6a61b004faa9903c5116ab39 https://doi.org/10.1093/humrep/deag116

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1Assessing the Accuracy of Large Language Models on European Guidelines for Cervical Cancer: An In Silico Benchmarking Study2025 · 7 citations
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3Responses of large language models to common questions on premature ovarian insufficiency: A comparative analysisOriginal Research · 2026
4Accuracy and Safety of Large Language Models in Endometrial Cancer Decision Making: A Case-Based In Silico Benchmarking StudyOriginal Research · 2026
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