![[2602.16050] Evidence-Grounded Subspecialty Reasoning: Evaluating a Curated Clinical Intelligence Layer on the 2025 Endocrinology Board-Style Examination](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16050] Evidence-Grounded Subspecialty Reasoning: Evaluating a Curated Clinical Intelligence Layer on the 2025 Endocrinology Board-Style Examination
Summary
This article evaluates the performance of the January Mirror, an evidence-grounded clinical reasoning system, against leading large language models on an endocrinology board-style exam, demonstrating superior accuracy and evidence traceability.
Why It Matters
The study highlights the challenges of subspecialty clinical reasoning in medicine, emphasizing the importance of curated evidence in improving diagnostic accuracy. As AI continues to evolve in healthcare, understanding how these systems perform against established benchmarks is crucial for future clinical applications.
Key Takeaways
- January Mirror outperformed leading LLMs on an endocrinology exam with 87.5% accuracy.
- The system demonstrated effective evidence traceability, citing guidelines accurately.
- Curated evidence can enhance clinical reasoning compared to real-time web access.
- The study underscores the importance of structured reasoning in medical AI applications.
- Results suggest potential for improved auditability in clinical deployments.
Computer Science > Artificial Intelligence arXiv:2602.16050 (cs) [Submitted on 17 Feb 2026] Title:Evidence-Grounded Subspecialty Reasoning: Evaluating a Curated Clinical Intelligence Layer on the 2025 Endocrinology Board-Style Examination Authors:Amir Hosseinian, MohammadReza Zare Shahneh, Umer Mansoor, Gilbert Szeto, Kirill Karlin, Nima Aghaeepour View a PDF of the paper titled Evidence-Grounded Subspecialty Reasoning: Evaluating a Curated Clinical Intelligence Layer on the 2025 Endocrinology Board-Style Examination, by Amir Hosseinian and 5 other authors View PDF HTML (experimental) Abstract:Background: Large language models have demonstrated strong performance on general medical examinations, but subspecialty clinical reasoning remains challenging due to rapidly evolving guidelines and nuanced evidence hierarchies. Methods: We 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. Mirror operated under a closed-evidence constraint without external retrieval. Comparator LLMs had real-time web access to guidelines and primary literature. Results: Mirror achieved 87.5% accuracy (105/120; 95% CI: 80.4-92.3%), exceeding a human reference of 62.3% and frontier LLMs including GPT-5.2 (74.6%), GPT-...
