Advancing Explainable AI in Deep Learning for Medical Imaging: Enhancing Transparency, Trust, and Clinical Utility

In particular, this review gives a complete study of Explainable Artificial Intelligence (XAI) in deep learning applied to medical imaging diagnostics, which are of key importance in AI powered clinical decisions. The study presents an exhaustive yet practical exploration of a range of XAI methodologies such as gradient based visualization techniques, perturbation-based models, attribution mechanisms, attention-based networks, surrogate models and hybrids in applying to understand and trust medical XAI. The explainability significantly increases model interpretability and clinician acceptance and its practical use case on disease detection, segmentation, and prognostic analytics are then discussed. In addition to main challenges such as balance accuracy with interpretability, data quality constraints, algorithmic bias and the regulatory barriers, the paper addresses how data holes can be addressed. Furthermore, areas in multimodal data fusion, human in the loop AI, privacy preserving learning and federated AI are also explored to see how they may increase the model robustness and scalability. Performance, interpretability, computational efficiency and clinical utility of XAI techniques are compared among each other, and are presented to aid in the selection of suitable models for specific medical imaging applications.