Advancing Explainable AI for Clinical Decision Support: A Multimodal Evaluation Framework

The increasing integration of Artificial Intelligence (AI) into Clinical Decision Support Systems (CDSS) is constrained by the limited transparency of model predictions, which undermines clinician trust and slows adoption in safety-critical settings. To address this barrier, we propose a unified multimodal evaluation framework for eXplainable AI (XAI) and empirically assess the behavior of the explanations in four clinically relevant modalities: chest radiography for pathology detection, Electroencephalography (EEG)-based epilepsy decision support using High-Frequency Oscillation (HFO) evidence, multimodal emotion recognition for psychological decision support and prediction of Alzheimer's disease based on Electronic Health Records (EHR). The framework evaluates widely used explanation mechanisms, including Gradient-weighted Class Activation Mapping (Grad-CAM), Integrated Gradients (IG), SHapley Additive exPlanations (SHAP)-style feature attribution, and attention-based interpretation, using modality-appropriate criteria that emphasize reliability, robustness, and clinical plausibility rather than visualization quality alone. The results show that Grad-CAM provides stable region-le