Mirror 在內分泌委員會考試中勝過 GPT-5

• Reasoning Architecture: Ensemble-style clinical reasoning stack with multiple specialized reasoning components generating candidate answers and supporting evidence links, followed by arbitration layer using evidence quality and internal agreement signals • Question Archetype Organization: Components structured around common clinical reasoning tasks—diagnosis, testing, treatment, prognosis, and mechanistic reasoning—reflecting how clinicians approach distinct problem types • Evidence Integration: System integrates curated endocrinology and cardiometabolic evidence corpus with structured reasoning architecture to generate evidence-linked outputs; operates under closed-evidence constraint without external retrieval • Output Design: Outputs include traceable citations from guidelines with 100% citation accuracy verification; 74.2% of outputs cited guideline sources • Performance Metrics: Top-2 accuracy of 92.5% vs GPT-5.2's 85.25%, indicating strong confidence in alternative diagnoses • Regulatory Alignment: Design aligns with FDA's January 2026 'Glass Box' CDS guidance requiring transparent, clinician-reviewable logic and verifiable source grounding rather than hallucinated citations[3]

Mirror's performance demonstrates that domain-specific evidence curation and structured clinical reasoning can achieve subspecialty-level performance exceeding general-purpose frontier LLMs, suggesting a strategic shift toward specialized clinical AI systems rather than relying on general-purpose models with web access. The January 2026 FDA guidance shift toward 'Glass Box' transparency requirements creates regulatory tailwinds for evidence-grounded systems like Mirror while restricting black-box approaches. This validates a design philosophy where AI augments rather than replaces clinician judgment through transparent reasoning. The closed-evidence advantage over web-enabled LLMs indicates that curated, high-quality evidence corpora may outperform real-time information access in specialized domains. Healthcare organizations may increasingly adopt domain-specific clinical reasoning systems for subspecialty applications, particularly in high-stakes diagnostic and treatment planning contexts where explainability and citation accuracy are regulatory and clinical requirements. The framework's success on difficult questions suggests potential for AI-assisted continuing medical education and quality improvement in areas where even subspecialty physicians struggle.