Explainable AI-driven Deep Learning for Neurological Disease Diagnosis using MRI: A systematic review and future directions

This systematic literature review examines the transformative impact of deep learning and explainable AI (XAI) on neurological disease diagnosis using MRI. We analyzed 180 studies from prominent databases, including IEEE Xplore, ScienceDirect, Google Scholar, PubMed, and Scopus, focusing on the methodologies, applications, and emerging trends in diagnosing brain tumors, Alzheimer's disease, and Parkinson's disease. Our findings reveal the increasing use of XAI techniques like Grad-CAM, LIME, and SHAP to enhance model transparency and trustworthiness, a crucial step towards clinical adoption. While deep learning models demonstrate promising diagnostic accuracy, challenges persist, including limited datasets, high computational demands, and the need for robust clinical validation. This review highlights the potential of multimodal data integration and the importance of developing computationally efficient and interpretable models. We identify key future directions, emphasizing the need for larger, more diverse datasets, advancements in XAI methodologies, and the development of personalized treatment strategies guided by AI-driven insights. This comprehensive analysis serves as a valuable resource for researchers and clinicians, offering a roadmap for future research and the responsible implementation of AI in neurological disease diagnosis.