DoAtlas-1：一种用于临床AI的因果编译范式
DoAtlas-1: A Causal Compilation Paradigm for Clinical AI
AI总结：
DoAtlas-1通过因果编译范式将医学证据转化为可执行代码，提升临床AI的可审计性和可验证性。
摘要：
医学基础模型能够生成叙述性解释，但无法量化干预效应、检测证据矛盾或验证文献主张，限制了临床可审计性。我们提出因果编译，一种将医学证据从叙述文本转化为可执行代码的范式。该范式将异质研究证据标准化为结构化的估计对象，每个对象明确指定干预对比、效应规模、时间跨度和目标人群，支持六个可执行的因果查询：do-演算、反事实推理、时间轨迹、异质效应、机制分解和联合干预。我们通过DoAtlas-1实现这一范式，通过效应标准化、矛盾意识图构建和现实世界验证（人类表型项目，10,000名参与者）将1,445个效应内核从754项研究中编译出来。系统实现了98.5%的标准化准确性以及80.5%的查询可执行性。该范式将医学AI从文本生成转向可执行、可审计和可验证的因果推理。
英文摘要：
Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical auditability. We propose causal compilation, a paradigm that transforms medical evidence from narrative text into executable code. The paradigm standardizes heterogeneous research evidence into structured estimand objects, each explicitly specifying intervention contrast, effect scale, time horizon, and target population, supporting six executable causal queries: do-calculus, counterfactual reasoning, temporal trajectories, heterogeneous effects, mechanistic decomposition, and joint interventions. We instantiate this paradigm in DoAtlas-1, compiling 1,445 effect kernels from 754 studies through effect standardization, conflict-aware graph construction, and real-world validation (Human Phenotype Project, 10,000 participants). The system achieves 98.5% canonicalization accuracy and 80.5% query executability. This paradigm shifts medical AI from text generation to executable, auditable, and verifiable causal reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
