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%; P<.001). High-performing AI systems (ChatGPT-01-preview, GPT4o, and Claude Sonnet 3.5) achieved consistently high cross-linguistic accuracy (88.33%) with minimal language impact (6.67%±0.00%). Resident performance declined significantly under time constraints (from 73.2% to 56.5% adjusted accuracy; Cohen's d=1.009; P<.001), whereas AI systems reported lesser deterioration. Error pattern analysis indicated a moderate correlation between AI and human reasoning (r=0.666; P<.001). Residents exhibited systematic progression from year 1 (44.7%) to year 5 (87.1%). Integration analysis found variable benefits across training levels, with maximum enhancement in early-career residents (+29.7%; P<.001). 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.