Background: AI models used in the clinic should be both accurate and explainable to the clinician, agency/regulatory officials, and patient. Despite a wide range of approaches developed in the field of Explainable AI (XAI) to explain models after the fact, create inherently interpretable models, and produce concept-based attributions, comprehensive evidence synthesis of the clinical performance and user acceptance of these methods is lacking. Objective: To comprehensively synthesize and meta-analyse studies of XAI methods for clinical healthcare AI from January 2017 to December 2025. Methods: We conducted a literature search in PubMed/MEDLINE, Embase, CINAHL, IEEE Xplore, and Scopus and found 104 eligible studies that were subject to qualitative synthesis (and meta-analysis of 78). Cochrane framework was used to assess the risk of bias. Results: SHAP and Grad-CAM are the most popular XAI methods used (41.3% and 28.8% of studies respectively). The highest scores of clinician agreement (pooled mean: 86.3%, 95% CI: 83.1–89.5) are obtained by prototype-based methods (ProtoPNet-Med). The proposed ClinXAI framework, which integrates concept bottleneck modelling and counterfactual clinica
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