Reinforcement Learning–Guided Nanorobotic Drug Delivery for Adaptive Tumor Targeting in Digital Twin– Enabled Virtual Health Environments

In precision oncology, precisely targeting tumors remains a major challenge due to heterogeneous tumor microenvironments, disease dynamics, limited adaptability of traditional nanocarriers, and inadequate therapeutic localization, which together lead to suboptimal therapeutic efficacy and systemic toxicity. Overcoming these barriers will be important for advancing the field of personalized cancer care and optimizing therapies in real time in next-generation virtual healthcare systems. To adaptively target the tumour and make intelligent treatment decisions, the Nanorobotic Drug Delivery (NDD) is guided by Reinforcement Learning (RL) embedded in a Digital Twin–Enabled Virtual Health Environment (VHE). The proposed architecture uses a deep reinforcement learning agent to continuously learn to optimize nanorobot navigation, payload release, and trajectory planning based on patient-specific physiological states, which are modeled by a dynamic digital twin. A synthetic multimodal oncology dataset was created with 12,000 instances of patient-specific simulation data covering tumor morphology, vascular characteristics, pharmacokinetic parameters, and treatment response, and was used to validate the framework. The experimental results show the high accuracy of targeting the tumor (96.8%), the efficiency of the drug delivery (94.5%), the high success rate in the navigation (97.2%) and the high accuracy of the prediction of the response to the treatment (95.6%), with a significant reduction of offtarget drug accumulation (38.4%) compared to conventional optimization methods. Moreover, the model proposed in the research achieved an F1 Score of 95.1%, a precision of 96.3%, a recall of 94.0%, and an average reward improvement of 42.7% in the course of policy convergence. Its most significant advantages are its ability to adapt in real time, personalise it for each patient, make it autonomous, and seamlessly integrate digital twins. Such functions can contribute to accurate and precise therapy, reduce side effects, promote clinical success and enable data-driven, scalable cancer therapy in future intelligent healthcare systems.