Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation

Researchers developed an online adaptive clinical decision support AI system that integrates treatment effect estimation, patient digital twins, and reinforcement learning to recommend treatments in real-time while ensuring safety. The system, validated on synthetic data and real ovarian cancer data from TCGA, outperformed standard baselines in effectiveness and stability, requiring expert consultation for only a minority of cases.

arXiv:2606.17405v1 Announce Type: new 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.