REINFORCEMENT LEARNING IN IOT-ENABLED HEALTHCARE: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS
Keywords:
Reinforcement Learning, , Internet of Things, Smart Healthcare, Deep Reinforcement Learning, Remote Patient Monitoring, Personalized Healthcare, Healthcare Optimization, Data Privacy and SecurityAbstract
Reinforcement Learning (RL), a powerful machine learning approach, is transforming healthcare by enabling intelligent systems that learn and adapt through continuous interaction with dynamic data environments. Unlike static heuristic methods, RL updates decisions based on real-time feedback, making it well-suited for medical settings where patient conditions evolve and treatment effects are delayed. This review examines 27 validated studies showcasing RL applications in real-time monitoring, critical care, drug dosing, personalized treatment, and more. It highlights how RL enhances clinical outcomes and automates complex decisions while addressing key challenges such as interpretability, ethical concerns, and integration with traditional healthcare systems. The study also discusses explainable RL and human-in-the-loop learning as vital steps toward bridging the gap between algorithmic intelligence and practical clinical use.
