Various Techniques for Explainable AI Evaluation Metrics and Benchmarks for Enhancing Predictive Analytics in Healthcare

The integration of Artificial Intelligence (AI) in healthcare has significantly improved predictive analytics. It is used for disease diagnosis, risk assessment, and clinical decision-making. However, many AI models operate as black-box systems. These are creating challenges related to interpretability, transparency, trust, accountability, and regulatory acceptance. Explainable Artificial Intelligence (XAI) has emerged as a promising approach to address these concerns. XAI provides understandable explanations of model behavior and predictions. Despite growing adoption of XAI techniques in healthcare. The evaluation of explainability remains fragmented. It  is  due to the absence of standardized metrics, benchmark datasets, and unified assessment frameworks. Existing studies primarily focus on isolated technical metrics. It is neglecting clinical relevance, usability, fairness, and safety considerations. This study presents a systematic literature review. The research analyzes existing parameters of concern like quantitative and qualitative evaluation metrics, benchmark protocols, and clinical validation approaches. They are used in recent healthcare AI studies. Furthermore, the study identifies critical gaps between research-level XAI implementations and real-world clinical deployment requirements.