Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation
Researchers propose an online adaptive framework integrating treatment effect estimation, patient digital twin, and reinforcement learning for clinical decision support. The system ensures safety via rule-based monitoring and outperforms baselines on synthetic and real ovarian cancer datasets, demonstrating potential for personalized medicine.
[2606.17405] Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation
[Submitted on 16 Jun 2026]
Title:Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation
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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.
Comments: Accepted for presentation at the IEEE Engineering in Medicine and Biology Conference (EMBC) 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.17405 [cs.AI]
(or arXiv:2606.17405v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.17405
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xinyu Qin [view email] [v1] Tue, 16 Jun 2026 01:39:55 UTC (1,674 KB)
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