基于证据的亚专业推理：评估2025年内分泌学委员会风格考试中的临床智能层

Large language models have shown strong performance on general medical examinations; however, subspecialty clinical reasoning remains challenging due to rapidly evolving guidelines and nuanced evidence hierarchies. This study evaluated January Mirror, an evidence-grounded clinical reasoning system, against frontier LLMs (GPT-5, GPT-5.2, Gemini-3-Pro) on a 120-question endocrinology board-style examination. Mirror integrates a curated endocrinology and cardiometabolic evidence corpus with a structured reasoning architecture to generate evidence-linked outputs. It operated under a closed-evidence constraint without external retrieval, while comparator LLMs had real-time web access to guidelines and primary literature.