A Comprehensive Review of Existing Smart Summary Recommendation Model Enhanced Through RL and XAI Techniques
S. M. Nandha Gopal, S. Sarumathi, Anjelina Tomy, S Damini, P S Harshitha, H Jyothi · 2024
This research work presents an innovative healthcare recommendation system designed to address the challenge of suboptimal feedback and medical errors. By integrating Reinforcement Learning (RL) and Explainable AI (XAI), the system aims to improve the accuracy and reliability of recommendations. The RL component enables the system to learn from real-world interactions, optimizing recommendations over time. XAI provides transparent explanations for model decisions, fostering trust and understanding among healthcare providers. This combination allows for personalized treatment plans, early disease detection, and efficient resource allocation. By addressing the limitations of traditional systems, this enhanced approach has the potential to revolutionize healthcare delivery, leading to improved patient outcomes and more efficient healthcare systems.