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, Dimitrios Rafail Kalaitzopoulos, S. Kamm, Mark Ormos, Daniel Passweg, Sabrina Schraag, E P Samartzis, Nicolas Samartzis, Isabell Witzel, Patrick Imesch
2025 · doi:10.5167/uzh-292465 · W7131309994
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⚙ AI-generated summary by claude@2026-06+body, 2026-06-17 ⓘ

This paper compares how artificial intelligence and human specialists answer endometriosis-related questions, using an anti-bot system that requires JavaScript to function.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-10 · read from full text ⓘ

This paper compared responses generated by large language models with those from human specialists to endometriosis-related questions, assessing how well AI could answer patient-style queries in a digital information context. The study found that AI performance was not inferior to human specialists in addressing these endometriosis-related queries, though reviewers could significantly distinguish AI-generated from human-generated responses. A major limitation reported was that interrater agreement was only slight, indicating variability in how evaluators judged the outputs. This paper is centrally about endometriosis—specifically, evaluating AI versus human specialist responses to endometriosis-related queries in “endometriosis care.”

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endometriosis

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last seen: 2026-06-10T17:14:06.276822+00:00
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