Autonomous AI-Driven Clinical Decision System Using Reinforcement Learning and Real-Time Patient Feedback

Abstract

The increasing complexity of clinical decision-making requires intelligent systems capable of adapting to dynamic patient conditions. This paper presents an Autonomous AI-Driven Clinical Decision System that leverages reinforcement learning and real-time patient feedback to optimize treatment strategies. The proposed framework models clinical decision-making as a sequential process, where an intelligent agent learns optimal treatment policies by interacting with patient data and outcomes. Reinforcement learning techniques, including deep Q-networks and policy optimization methods, are employed to handle complex and uncertain healthcare environments. The system continuously updates its knowledge based on real-time patient responses, enabling personalized and adaptive treatment recommendations. Integration with electronic health records and wearable devices allows continuous monitoring and feedback-driven learning. Additionally, safety constraints and explainability mechanisms are incorporated to ensure clinical reliability and trust. Experimental results demonstrate that the proposed approach improves treatment outcomes and decision accuracy compared to static models. Integration with prior research in healthcare analytics further enhances system robustness and generalization. The study highlights the potential of autonomous AI systems in transforming healthcare into a proactive and adaptive decision-making ecosystem.