让AI在医疗诊断中既准确又透明，还能说明不确定性的来源。
Medical Model Synthesis Architectures: A Case Study
- 用语言模型找知识，再构建概率模型做推理
- 能给出带置信度的诊断列表，区分可能性高低
- 适合需要可解释决策的临床AI系统开发
医学充满高风险的不确定性。医生常需在诸多未知情况下做出判断，如推测症状原因或选择下一步治疗。尽管越来越多AI系统被用于辅助甚至替代医生，但现有系统在不确定性下的校准推理能力不足，且推理过程常不透明。我们提出一种新框架MedMSA，可在不确定性下生成既实用又形式化透明的临床预测。给定临床情境，该框架利用语言模型检索相关先验知识，并构建正式的概率模型以支持校准且可验证的推断。我们展示了该框架的初步概念验证，可用于鉴别诊断，生成基于不确定性的潜在诊断列表，解释患者症状；并讨论其未来在安全临床协作中的广泛适用方向。
原文摘要 · Abstract (English)
Medicine is rife with high-stakes uncertainty. Doctors routinely make clinical judgments and decisions that juggle many fundamental unknowns, like predictions about what might be causing a patients' symptoms or decisions about what treatment to try next. Despite increasing interest in developing AI systems that aid or even replace doctors in clinical settings, current systems struggle with calibrated reasoning under uncertainty, and are often deeply opaque about their reasoning. We propose a framework for AI systems that can make practically useful but formally transparent clinical predictions under uncertainty. Given a clinical situation, our framework (MedMSA) uses language models to retrieve relevant prior knowledge, but constructs a formal probabilistic model to support calibrated and verifiable inferences under uncertainty. We show how an initial proof-of-concept of this framework can be used for differential diagnosis, producing an uncertainty-weighted list of potential diagnoses that could explain a patients' symptoms, and discuss future applications and directions for applying this framework more generally for safe clinical collaborations.
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