🤖 AI Summary
This study addresses the challenge of enabling clinical decision support systems to safely and dynamically adapt to evolving patient conditions in real time while optimizing treatment strategies. The authors propose the first online adaptive AI framework that integrates patient-specific digital twins with reinforcement learning, leveraging treatment effect estimation, a pre-trained outcome prediction model, and a rule-based engine to generate personalized sequential decisions. To ensure clinical safety, the framework incorporates an expert review mechanism that triggers human oversight only when necessary. Experimental results on both a synthetic simulator and the TCGA ovarian cancer dataset demonstrate that the proposed system significantly outperforms baseline approaches, achieves low decision latency, and requires manual intervention in only a small fraction of cases, thereby validating its efficacy and clinical feasibility.
📝 Abstract
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.