AI 中文总结
EviDx是结合患者诊断环境、临床诊断支架与运行时控制模块的证据感知主动诊断框架，可提升LLM的诊断性能与过程稳定性，还能揭示模型的能力边界。
AI 中文摘要
临床诊断是一个主动的证据获取过程，临床医生会获取证据、更新竞争假设，并判断现有证据是否足以做出诊断。然而，许多基于大语言模型（LLM）构建的医疗诊断系统仍将诊断视为静态的从病例到答案的预测，对证据获取的支持有限。智能体式LLM通过工具使用和中间诊断轨迹提供了一种动态替代方案，但现有系统通常未明确规定患者证据在运行时应如何暴露、支架化和控制。我们提出EviDx，这是一个证据感知的主动诊断框架，将患者特定的诊断环境与临床诊断支架、观察者引导的运行时控制模块相结合。在EviDx中，$\boldsymbol{\textit{E}}$-Synthesis从原始临床病例构建交互环境；支架组织角色专业化智能体、证据工具和不断演变的证据状态；运行时控制模块通过跟踪不确定性和证据覆盖范围来调节诊断终止。一个三级评估金字塔评估执行鲁棒性、推理动态和诊断结果。实验表明，EviDx可提升诊断性能和过程稳定性，同时揭示了依赖于模型的能力边界。
英文摘要
Clinical diagnosis is an active evidence-seeking process in which clinicians acquire evidence, update competing hypotheses, and decide when the available evidence is sufficient for diagnosis. Yet many medical diagnosis systems built around large language models (LLMs) still formulate diagnosis as static case-to-answer prediction, with limited support for evidence acquisition. Agentic LLMs offer a dynamic alternative through tool use and intermediate diagnostic trajectories, but existing systems often under-specify how patient evidence should be exposed, scaffolded, and controlled at runtime. We introduce EviDx, an evidence-aware active diagnosis framework that pairs patient-specific diagnostic environments with a clinical diagnostic scaffold and an observer-guided runtime harness. In EviDx, $\mathcal{E}$-Synthesis constructs interactive environments from raw clinical cases; the scaffold organizes role-specialized agents, evidence tools, and evolving evidence states; and the harness regulates diagnostic termination by tracking uncertainty and evidence coverage. A 3-level evaluation pyramid assesses execution robustness, reasoning dynamics, and diagnostic outcomes. Experiments show that EviDx improves diagnostic performance and process stability while revealing model-dependent capability boundaries.
