Transparency in Medical Recommendations: A Comprehensive Methodology of Explainable AI Techniques in Healthcare

Abstract

Explainable AI (XAI) techniques are increasingly crucial in healthcare for enhancing transparency and trust in medical recommendations. XAI refers to methodologies and tools that make the decision-making process of AI systems transparent and comprehensible to humans. This paper reviews and proposes a comprehensive methodology to improve the explainability and trustworthinessof healthcare recommender systems. Key components include advanced machine learning models, such as Convolutional Neural Networks and Restricted Boltzmann Machines, integrated with explainable AI techniques like LIME and SHAP. The system incorporates collaborative, content-based, and graph-based filtering for personalized recommendations, supported by robust UI/UX design principles. The methodology aims to bridge the gap between AI-driven recommendations and user trust, thereby enhancing patient outcomes and healthcare delivery efficiency.