Explainable AI in Healthcare: A Comparative Analysis of Interpretability Techniques for Clinical Decision Support Systems

Abstract

Artificial intelligence has made a great impact on healthcare by providing accurate disease diagnosis, personalised treatment regimens, and efficient clinical decision making. But many of the advanced machine learning and deep learning models are black-box systems, and healthcare professionals find it difficult to understand the logic behind their predictions. This opacity hinders the adoption of intelligent systems in clinical settings where trust and accountability are a must. In this review paper we compare the main interpretability techniques that have been used in clinical decision support systems. These techniques include Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), saliency maps, Gradient-weighted Class Activation Mapping (Grad-CAM), attention mechanisms, and decision trees, among others. We performed a systematic literature review to evaluate these techniques based on interpretability, computational complexity, scalability, transparency, and clinical relevance. A systematic literature review was performed to evaluate the techniques in terms of interpretability, computational complexity, scalability, transparency and clinical relevance. The analysis shows that SHAP provides complete local and global explanations, while LIME provides computationally efficient local interpretations. Visualisation based methods such as Grad-CAM and saliency maps are especially useful for medical image analysis, while attention mechanisms are suitable for sequential healthcare data. The study concludes that explainable artificial intelligence improves trust, reliability, and accountability in healthcare systems and is a prerequisite for successful integration of intelligent technologies into clinical practice. Keywords: machine learning; clinical decision support systems; Explainable Artificial Intelligence; Healthcare Analytics; interpretability