VeriDx: Earning the Right to Diagnose with Disease-Centric Verification

2026-09-12

AI Summary

本文提出VeriDx框架，通过疾病中心验证解决医学LLM诊断中因忽略假设引起的义务而导致的错误，如遗漏关键测试等。

Abstract

A correct diagnosis can still be reached for the wrong reasons. In clinical reasoning, every disease hypothesis creates obligations: key evidence must be checked, alternatives must be ruled out, contradictions must be resolved, useful tests must be considered, and closure must be justified. Current evaluations of medical LLMs mostly focus on final answers, local steps, or isolated facts, and therefore miss these hypothesis-induced commitments. We introduce \textbf{VeriDx}, a disease-centric verification framework that links free-form diagnostic reasoning to structured disease profiles. VeriDx tracks whether each hypothesis is satisfied, unresolved, or violated its clinical obligations, exposing failures such as missing critical tests, unresolved differentials, ignored contradictions, unsupported claims, and premature closure. We instantiate VeriDx for complex respiratory diagnosis using guideline-derived disease profiles and expert-annotated longitudinal cases. Our results show that many diagnostic errors are not isolated mistakes, but broken commitments made earlier in the reasoning process.

Problem

• diagnostic reasoning
• clinical obligations
• hypothesis-induced commitments
• medical LLMs
• disease hypothesis

Innovation

• VeriDx
• disease-centric verification
• diagnostic reasoning
• clinical obligations
• hypothesis-induced commitments