Explainable Artificial Intelligence in Healthcare: Methods, Challenges, and a Conceptual Framework

Explainable Artificial Intelligence (XAI) is vital for enabling trust, transparency, and accountability in medical decision support systems. This paper provides a conceptual survey of core XAI techniques— such as LIME, SHAP, Integrated Gradients, Grad-CAM, counterfactual explanations, and concept activation—and analyzes their theoretical foundations, strengths, and limitations in healthcare settings. We further discuss major challenges unique to clinical deployment: explanation validation, human-AI interaction, trust calibration, and regulatory compliance. To guide researchers and practitioners, we propose a conceptual framework mapping XAI methods by clinical task (diagnosis, prognosis, treatment decision), data modality (tabular, imaging, multimodal), and explanation scope (local vs global). We conclude with recommendations for future work: benchmark datasets for explanation evaluation, multimodal interpretation, human‐centered studies, and integration with causal reasoning.