A Comprehensive Review on Explainable Artificial Intelligence (XAI) Techniques for Healthcare Diagnosis and Decision Support Systems
Keywords:
Clinical decision support, EEG, Explainable AI (XAI), Grad-CAM, Healthcare, Interpretability, LIME, Medical imaging, SHAPAbstract
Explainable Artificial Intelligence (XAI) has become a critical research area for deploying AI in healthcare, where safety, accountability, trust, and regulatory compliance are essential. This paper reviews XAI approaches applied to healthcare diagnosis and decision support systems, provides a taxonomy of methods (model-intrinsic vs model-agnostic; global vs local; visual vs attributional vs rule-based), compares representative techniques (SHAP, LIME, Grad-CAM, Integrated Gradients, attention mechanisms, rule/exemplar approaches), and surveys application domains including medical imaging, electronic health records (EHR), and physiological signal analysis (e.g., EEG). We synthesise comparative strengths/limitations, summarise clinical evaluation strategies, and identify open research gaps — including clinical validation, standardised XAI evaluation metrics, multimodal explanations, and regulatory alignment. Finally, we propose future directions to accelerate clinically-trusted XAI adoption.
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