ArticleMayo Clinic proceedings. Digital health2025
The PERFORM Study: Artificial Intelligence Versus Human Residents in Cross-Sectional Obstetrics-Gynecology Scenarios Across Languages and Time Constraints.
Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence in Obstetrics: Current Trends and Future Directions.Journal of clinical medicine · 2026Review
- Comparative feasibility of reasoning and non-reasoning large language models for gynecologic cancer emergency care.BMC health services research · 2025Article
- A Robust Framework for Domain-Generalized Classification of Ovarian Cancer Histology Images.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To systematically evaluate the performance of artificial intelligence (AI) large language models (LLMs) compared with obstetrics-gynecology residents in clinical decision-making, examining diagnostic accuracy and error patterns across linguistic domains, time constraints, and experience levels. Patients and Methods: In this cross-sectional study, we evaluated 8 AI LLMs and 24 obstetrics-gynecology residents (Years 1-5) using 60 standardized clinical scenarios. Most AI LLMs and all residents were assessed in May 2024, whereas chat GPT-01-preview, chat-GPT4o, and Claude Sonnet 3.5 were evaluated in November 2024. The assessment framework incorporated English and Italian scenarios under both timed and untimed conditions, along with systematic error pattern analysis. The primary outcome was diagnostic accuracy; secondary end points included AI system stratification, resident progression, language impact, time pressure effects, and integration potential. Results: The AI LLMs reported superior overall accuracy (73.75%; 95% confidence interval [CI], 69.64%-77.49%) compared with residents (65.35%; 95% CI, 62.85%-67.76%; Conclusion: High-performing AI LLMs reported strong diagnostic accuracy and resilience under linguistic and temporal pressures. These findings suggest that AI-enhanced decision-making may offer particular benefits in obstetrics and gynecology training programs, especially for junior residents, by improving diagnostic consistency and potentially reducing cognitive load in time-sensitive clinical settings.
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Registered trials
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