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Head and neck cancer radiotherapy requires highly precise dose delivery to ensure tumor control while sparing nearby critical structures, but daily anatomical changes such as tumor shrinkage, weight loss, and setup variability often degrade treatment accuracy. Although cone-beam CT provides valuable daily imaging, current adaptive radiotherapy workflows remain largely manual, time-consuming, and infrequent, limiting their ability to respond to ongoing anatomical changes and often resulting in suboptimal target coverage or increased toxicity risk. To address these limitations, we propose a deep reinforcement learning framework for fully automated daily treatment adaptation using cone-beam CT and dosimetric constraints. The problem is formulated as a sequential decision-making task in which an agent adjusts beam parameters based on evolving patient anatomy, cumulative dose, and constraint satisfaction. The state includes daily imaging and dose history, the action space involves fluence or multileaf collimator adjustments, and the reward function balances target coverage, organ-at-risk sparing, and plan stability. A patient-specific simulator based on historical imaging enables training without real-time patient interaction. This framework enables continuous, personalized, and automated plan adaptation that directly responds to anatomical changes while maintaining clinical safety constraints. By leveraging long-horizon optimization, the system can outperform static planning strategies and better manage stochastic anatomical variations in head and neck cancer treatment. Overall, this approach provides a foundation for closed-loop adaptive radiotherapy that could improve treatment accuracy, reduce toxicity, and reduce reliance on manual planning.

Atrial fibrillation affects over 30 million people worldwide and requires long-term anticoagulation, with warfarin still widely used due to its efficacy and reversibility, but its narrow therapeutic window (INR 2.0–3.0) makes dosing particularly challenging, especially in high bleeding-risk patients where both under- and over-anticoagulation can lead to serious complications. Conventional dosing approaches rely on population-based nomograms and clinician judgment, failing to capture individual variability driven by genetics, diet, comorbidities, and drug interactions. To address this limitation, this article proposes a conceptual framework that integrates deep reinforcement learning with a safety-shield mechanism for personalized warfarin dosing. The system uses a deep Q-network trained on historical patient trajectories within an offline Markov Decision Process to recommend dose adjustments based on INR history and clinical risk factors, while a deterministic rule-based safety layer blocks unsafe actions, such as dose increases when INR exceeds 3.5 or extreme adjustments requiring clinician review. Conservative offline reinforcement learning further reduces the risk of unsafe policy extrapolation by limiting overestimation of out-of-distribution actions. Together, this hybrid architecture aims to improve time in therapeutic range while minimizing bleeding risk, providing a structured and clinically constrained approach for safer, individualized anticoagulation management in high-risk atrial fibrillation patients.

Personalized rehabilitation exercise prescriptions are essential for recovery after neurological injury, orthopedic surgery, and chronic decline. While physical therapists have valuable expertise, translating it into scalable computational systems is challenging. Standard deep reinforcement learning relies on manually defined reward functions, but in rehabilitation, clinically significant goals like movement quality, fatigue, pain, safety, motivation, and adherence are difficult to quantify. This paper introduces a framework combining inverse reinforcement learning (IRL) and deep reinforcement learning (DRL) to learn personalized rehabilitation prescriptions from therapist demonstrations. IRL would derive expert-aligned rewards, and DRL would use these to create adaptive exercise plans. The framework encompasses therapist demonstration collection, movement trajectory representation, reward inference, policy learning, safety constraints, and clinical oversight. Demonstrations would include exercise selection, progression decisions, and therapist responses to patient fatigue, pain, or adherence issues. IRL could capture implicit clinical priorities, while DRL would adjust prescriptions based on patient conditions such as fatigue, progress, and engagement. The framework aims to create scalable, personalized rehabilitation prescriptions, offering a conceptual model for future rehabilitation robotics, exergaming, and home-based digital rehabilitation systems.