The integration of Explainable Artificial Intelligence (XAI) into healthcare has significantly advanced clinical decision-making by enhancing the transparency and trustworthiness of AI-driven recommendations. This study introduces a novel Deep Reinforcement Learning (DRL) framework designed to generate personalized treatment recommendations tailored to individual patient profiles. The framework combines Deep Q-Learning and Policy Gradient methods to dynamically model and optimize treatment pathways, utilizing historical clinical data, patient demographics, and treatment response patterns. To ensure interpretability, an explainability layer incorporating SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) provides clinicians with actionable insights into the model’s decision-making process. The proposed framework was rigorously evaluated on a real-world dataset comprising 50,000 electronic health records (EHRs) from patients with cardiovascular disease and diabetes. Experimental results demonstrated a 28% improvement in treatment success rates, a 35% reduction in adverse effects, and a 20% increase in clinician acceptance compared to conventional rule-based methods. Additionally, the explainability module achieved an average accuracy of 92% in attributing model decisions to key patient features, reinforcing its reliability in clinical settings. These findings underscore the potential of the DRL-XAI framework to enhance patient outcomes while fostering trust in AI-assisted healthcare systems. By balancing predictive accuracy with interpretability, this approach addresses critical challenges in AI adoption, paving the way for more transparent and personalized clinical decision support tools. Future research will focus on extending the framework to additional medical conditions and integrating multi-modal patient data for broader applicability.
Thangarasan et al. (Sat,) studied this question.