Artificial Intelligence in Endometriosis Management: A Guideline-Concordance Study

Ezgi Darıcı, Miray Nilufer Cimsit Kemahli, Emine Karabük, Mehmet Faruk Köse
In: Cerasus Journal of Medicine · 2026 · doi:10.70058/cjm.1973205 · W7213296296
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An evaluation of fifteen endometriosis scenarios found that artificial intelligence generated recommendations with 93.3% overall concordance to ESHRE guidelines, demonstrating strong alignment in most domains despite variability in complex fertility cases.

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This study evaluated the concordance between artificial intelligence-generated clinical recommendations and the 2022 ESHRE Endometriosis Guideline using fifteen predefined clinical scenarios. The results demonstrated an overall concordance rate of 93.3%, with perfect alignment observed in medical management, surgical, and high-risk domains, while diagnostic and fertility-related scenarios showed slightly lower agreement. Although AI tools exhibited high inter-run consistency and meaningful alignment with evidence-based guidance, the authors noted variability in complex cases that limits their reliability as standalone decision-making aids. This paper is centrally about endometriosis — specifically evaluating how well artificial intelligence systems align with established international guidelines for its diagnosis and management.

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Abstract

Objective: This study aimed to evaluate how closely artificial intelligence (AI)- generated clinical recommendations correspond with established international guidelines for endometriosis management, using structured clinical scenarios.Methods: This study assessed the consistency of AI-generated responses with the 2022 ESHRE Endometriosis Guideline. Fifteen predefined clinical scenarios were designed to reflect the main areas of endometriosis management. Each scenario was submitted using a standardized prompt to ensure consistency. AI responses were evaluated using predefined guideline-based assessment matrices to determine concordance and to explore performance differences across clinical domains.Results: A total of 45 AI-generated responses across 15 clinical scenarios were evaluated. Overall concordance with ESHRE guideline-based reference answers was 93.3% (42/45). Diagnostic scenarios demonstrated 88.9% concordance, while medical management, surgical/multidisciplinary, and high-risk scenarios demonstrated 100% concordance. Fertility-related scenarios showed 83.3% concordance. Complete inter-run consistency was observed in 13 of 15 scenarios (86.7%), with variability limited to Cases 2 and 3. Fleiss’ kappa indicated a high level of inter-run agreement within the evaluated scenarios (κ = 0.831; 95% CI, 0.583–1.000).Conclusion: Artificial intelligence systems show meaningful alignment with evidence-based guidance in fundamental aspects of endometriosis management. However, variability in complex scenarios reveals important limitations. Although AI tools may support education or serve as an adjunct in clinical decision-making, they cannot replace expert clinical judgment in specialized gynecologic care.
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Abstract

Objective: This study aimed to evaluate how closely artificial intelligence (AI)- generated clinical recommendations correspond with established international guidelines for endometriosis management, using structured clinical scenarios.

Methods

This study assessed the consistency of AI-generated responses with the 2022 ESHRE Endometriosis Guideline. Fifteen predefined clinical scenarios were designed to reflect the main areas of endometriosis management. Each scenario was submitted using a standardized prompt to ensure consistency. AI responses were evaluated using predefined guideline-based assessment matrices to determine concordance and to explore performance differences across clinical domains.

Results

A total of 45 AI-generated responses across 15 clinical scenarios were evaluated. Overall concordance with ESHRE guideline-based reference answers was 93.3% (42/45). Diagnostic scenarios demonstrated 88.9% concordance, while medical management, surgical/multidisciplinary, and high-risk scenarios demonstrated 100% concordance. Fertility-related scenarios showed 83.3% concordance. Complete inter-run consistency was observed in 13 of 15 scenarios (86.7%), with variability limited to Cases 2 and 3. Fleiss’ kappa indicated a high level of inter-run agreement within the evaluated scenarios (κ = 0.831; 95% CI, 0.583–1.000).

Conclusion

Artificial intelligence systems show meaningful alignment with evidence-based guidance in fundamental aspects of endometriosis management. However, variability in complex scenarios reveals important limitations. Although AI tools may support education or serve as an adjunct in clinical decision-making, they cannot replace expert clinical judgment in specialized gynecologic care.

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References

- Giudice LC. Endometriosis. N Engl J Med. 2010;362(25):2389-98. doi:10.1056/NEJMcp1000274. - Zondervan KT, Becker CM, Missmer SA. Endometriosis. N Engl J Med. 2020;382(13):1244-56. doi:10.1056/NEJMra1810764. - De Graaff AA, D’Hooghe TM, Dunselman GAJ, Dirksen CD, Hummelshoj L, Simoens S, et al. The significant effect of endometriosis on physical, mental, and social well-being: results from an international cross-sectional survey. Hum Reprod. 2013;28(10):2677-85. doi:10.1093/humrep/det284. - Nnoaham KE, Hummelshoj L, Webster P, D’Hooghe T, de Cicco Nardone F, de Cicco Nardone C, et al. Impact of endometriosis on quality of life and work productivity: a multicenter study across ten countries. Fertil Steril. 2011;96(2):366-73.e8. doi:10.1016/j.fertnstert.2011.05.090. - Hadfield R, Mardon H, Barlow D, Kennedy S. Delay in the diagnosis of endometriosis: a survey of women from the USA and the UK. Hum Reprod. 1996;11(4):878-80. doi:10.1093/oxfordjournals.humrep.a019270. - Vercellini P, Buggio L, Frattaruolo MP, Borghi A, Dridi D, Somigliana E. Medical treatment of endometriosis-related pain. Best Pract Res Clin Obstet Gynaecol. 2018;51:68-91. doi:10.1016/j.bpobgyn.2018.01.015. - Becker CM, Bokor A, Heikinheimo O, Horne A, Jansen F, Kiesel L, et al. ESHRE guideline: endometriosis. Hum Reprod Open. 2022;2022(2):hoac009. doi:10.1093/hropen/hoac009. - Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PAC, et al. Why don’t physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999;282(15):1458-65. doi:10.1001/jama.282.15.1458. Details Primary Language English Subjects Clinical Sciences (Other) Journal Section Research Article Early Pub Date September 16, 2026 Publication Date - Submission Date June 18, 2026 Acceptance Date August 27, 2026 Published in Issue Year 2026 Number: Advanced Online Publication

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