Artificial intelligence in endometriosis care: A comparative analysis of large language model and human specialist responses to endometriosis-related queries

Laurin Burla, Julian Matthias Metzler, Kalaitzopoulos DR, Dimitrios Rafail Kalaitzopoulos, S. Kamm, Kamm S, Simone Kamm, Ormos M, Mark Ormos, Daniel Passweg, Passweg D, Sabrina Schraag, Schraag S, Samartzis EP, Eleftherios Pierre Samartzis, Nicolas Samartzis, Samartzis N, Isabell Witzel, Witzel I, Patrick Imesch, Imesch P
European journal of obstetrics, gynecology, and reproductive biology · 2025 · vol. 313 , pp. 114625 · doi:10.1016/j.ejogrb.2025.114625 · PMID:40829501 · W4412988157
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⚙ AI-generated summary by gemini-2.5-flash-lite, 2026-06-07 ⓘ

This study found that large language models were not inferior to human specialists in answering endometriosis-related queries, with no substantial differences observed in medical content despite reviewers being able to significantly distinguish between AI and human responses.

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Abstract

INTRODUCTION: Many women with endometriosis turn to digital sources for information. Meanwhile, large language models (LLMs) appear capable of offering health advice. This study aimed to evaluate the potential in answering endometriosis-related queries. METHOD: This comparative study used 150 anonymized endometriosis Q&As from online forums (2021-2023), answered by human specialists. Another 150 responses were generated using an LLM (ChatGPT-4o). Eight expert reviewers, split into two groups, blindly evaluated either the human or artificial intelligence (AI) response for each question. The primary endpoint was whether responses could be correctly identified as human or AI. Secondary endpoints included incorrect information, harm, and suitability for patient communication. RESULT: Each reviewer group assessed 600 responses; human and AI responses were differentiated significantly (χ2 = 246.162, p < 0.001, slight interrater agreement). Most responses were accurate (84.8 %), harmless (87.2 %), and patient-suitable (73.8 %). There were no significant differences regarding incorrect information (p = 0.308), harm likelihood (p = 0.944), harm extent (p = 0.892), medical consensus alignment (p = 0.235), or suitability for patients (p = 0.544). CONCLUSION: This study found that AI was not inferior to human specialists in answering endometriosis-related queries. While reviewers were able to distinguish AI- from human-generated responses significantly, interrater agreement was only slight. No substantial differences were observed in medical content. As AI continues to evolve, patients increasingly turn to it for medical guidance, highlighting the need for greater specialization in endometriosis care. Future research should further investigate the risks, benefits, and patient acceptance of AI in clinical practice.

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Condition tags

endometriosis

MeSH descriptors

Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

References (33)

Cited by (3)

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europepmc
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