Artificial intelligence has advanced rapidly in biomedicine through large-scale multimodal data integration, enabling increasingly accurate prediction of clinical outcomes and patient stratification. These systems, however, remain fundamentally observational: they learn statistical associations from historical data and operate within previously observed biological and clinical states, limiting their ability to generalize to novel therapies or unobserved interventions. We argue that AI in biomedicine is undergoing a structural transition. As biomedical decision-making increasingly depends on reasoning about intervention rather than extrapolation from past observations, predictive architectures become structurally insufficient. Systems that learn from historical data cannot, by construction, represent how biological systems evolve under perturbation, and therefore cannot reliably support decision-making in the presence of novel interventions. We introduce a conceptual framework distinguishing observational and interventional intelligence and define disease-level models as systems that explicitly represent the state, dynamics, and intervention response of biological processes. These models enable a shift from inference to simulation -- reasoning about what will happen under intervention rather than what is likely based on the past. This transition also implies a shift in where value is created: from data processing and prediction toward systems that support and define decision-making under intervention. It follows directly from the structure of biomedical decision-making and defines the next stage of AI in medicine. Systems that cannot model intervention will be structurally excluded from decision-making.

翻译：人工智能通过大规模多模态数据整合在生物医学领域取得了快速进展，使临床结果预测和患者分层日益精准。然而，这些系统本质上仍属于观察性：它们从历史数据中学习统计关联，并在先前观察到的生物和临床状态范围内运行，这限制了其对新型疗法或未观察干预的泛化能力。我们认为，生物医学中的人工智能正经历结构性转型。随着生物医学决策日益依赖于对干预的推理而非对过去观察的外推，预测性架构在结构上变得不足。从历史数据学习的系统在构建上无法表征生物系统在扰动下的演化方式，因此无法可靠地支持存在新型干预时的决策。我们提出一个概念框架，区分为观察性智能与干预性智能，并将疾病级模型定义为显式表征生物过程状态、动力学和干预响应的系统。这些模型实现了从推理到模拟的转变——即推理在干预下将会发生什么，而非基于过去可能发生什么。这一转型也意味着价值创造点的转移：从数据处理和预测转向支持和定义干预下决策的系统。这直接源于生物医学决策的结构，并定义了医学人工智能的下一个阶段。无法建模干预的系统将在结构上被排除在决策之外。