Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints. We present an online adaptive framework that integrates Treatment Effect (TE) estimation to quantify clinical benefits, a patient Digital Twin (DT) to simulate treatment trajectories, and Reinforcement Learning (RL) for sequential decision-making. The AI system is initially trained on historical medical records and operates in a continuous learning loop. To ensure safety, a rule-based module monitors vital signs and blocks contraindicated treatments. Cases with strong internal model disagreement are flagged for clinician review, simulated in our experiments via a pre-trained outcome model. We validate our framework using both a synthetic clinical simulator and a real-world ovarian cancer dataset from The Cancer Genome Atlas (TCGA). In both simulated and clinical settings, our method demonstrated superior effectiveness and stability in recommending treatments compared to standard computational baselines. Furthermore, the AI system maintains low latency and requires expert consultation for only a minority of cases in our experimental validation, demonstrating its potential as a safe, clinician-supervised tool for personalized medicine that continuously improves through practical use.

翻译：临床决策支持AI系统（CDSASs）必须在遵守严格安全约束的同时，实时适应不断变化的患者状态。我们提出了一种在线自适应框架，该框架整合了用于量化临床获益的处理效应（TE）估计、用于模拟治疗轨迹的患者数字孪生（DT）以及用于序贯决策的强化学习（RL）。该系统首先基于历史病历进行训练，并在持续学习循环中运行。为确保安全性，一个基于规则的模块会监测生命体征并阻断禁忌治疗。对于模型内部分歧较大的病例，系统会标记以供临床医生审查，在我们的实验中通过预训练的结果模型进行模拟。我们分别使用合成临床模拟器和来自癌症基因组图谱（TCGA）的真实卵巢癌数据集验证了该框架。在模拟环境和临床环境中，与标准计算基线相比，我们的方法在推荐治疗方面均展现出优越的有效性和稳定性。此外，该AI系统在我们的实验验证中保持了低延迟，且仅需对少数病例进行专家咨询，这证明了其作为一种安全、受临床医生监督的个性化医疗工具的潜力，并可通过实际应用持续改进。