Artificial Intelligence (AI) has become integral to healthcare, enabling disease prediction, medical imaging analysis, and personalized treatment planning. However, the opaque or “black-box” nature of many AI models-particularly deep learning-creates significant challenges for trust, safety, ethics, and regulatory acceptance. Explainable Artificial Intelligence (XAI) offers solutions by making AI decisions interpretable, transparent, and accountable. This paper presents a comprehensive study of XAI methods, tools, and applications specifically in the healthcare domain. Feature-based, concept-based, surrogate models, pixel-based explanations, and human-centric XAI approaches are explored in detail. Common XAI frameworks such as SHAP, LIME, ELI5, IBM AIF360, and the What-If Tool are evaluated for their role in clinical interpretability and fairness. Applications of XAI in Parkinson’s disease detection, cancer diagnostics, Alzheimer’s prediction, and COVID-19 risk assessment are reviewed, along with broader use cases in cardiovascular diagnostics and treatment planning. Key challenges such as interpretability– performance trade-offs, data bias, workflow integration, and ethical concerns are also analyzed. The paper concludes that XAI is essential for bridging the gap between AI technology and clinical decision-making. Future research directions include human-centered explainability, regulatory frameworks, real-time EHR integration, and next-generation interpretable deep learning architectures.
Explainable AI, Healthcare, SHAP, LIME, Medical Diagnosis, Transparency, Clinical Decision Support.
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