Introduction: Artificial intelligence (AI) has demonstrated transformative potential in medical education and assessment, with large language models achieving competitive results across multiple high-stakes examinations. In this study, we evaluated the performance and inter-run reliability of 10 widely adopted large language models (LLMs) on the European Board of Hand Surgery written examination. Methods: Ten LLMs were assessed on the complete 300-item European Board of Hand Surgery written examination using standardized zero-shot prompting. The models included five proprietary systems (GPT-5 Pro, Claude Sonnet 4.5, Gemini 2.5 Pro, Grok-4 and ERNIE 4.5 Turbo) and five open-source architectures (DeepSeek V3.2, Qwen3 Max, Mistral Medium 3.1, Llama 3.3 and Falcon H1). Each LLM completed five independent runs, producing 15000 answers analysed for mean accuracy, 95% confidence intervals and inter-run reliability using Cohen’s kappa ( κ ). Results: Mean accuracy across the LLMs ranged from 72 to 85%, corresponding to total European Board of Hand Surgery scores between 131 and 211 points. Seven of the 10 LLMs reached or exceeded the illustrative pass threshold of 75%, equivalent to 150 of 300 points. Proprietary systems showed consistently higher mean accuracy than open-source systems. The highest-performing LLM (GPT-5 Pro) achieved 85% accuracy with a 95% confidence interval of 84 to 86% and a mean inter-run reliability measured by Cohen’s κ of 0.739. The overall reliability across the LLMs was 0.821. Conclusions: Contemporary LLMs show robust and reproducible performance on a complex surgical certification examination, with proprietary architectures tending to outperform open-source counterparts. Although several models reached or exceeded an illustrative pass threshold, persistent gaps in subspecialty knowledge remain such as congenital anomalies and complex reconstructions. Therefore, LLMs may assist in structured learning and examination preparation but require specialist oversight and remain unsuitable for independent subspecialty decision-making. Level of evidence: Not applicable.
Güler et al. (Tue,) studied this question.