A Comprehensive Analysis on Enhancing Alzheimer Early Detection Using Explainable Artificial Intelligence

Alzheimer Disease (AD) is one of the most complicated neurodegenerative disorders which requires diagnostic systems with high accuracy and interpretability. The review is an extensive consideration of nineteen articles that implement Explainable Artificial Intelligence (XAI) to detect early AD using one or more modalities, including MRI, PET, fMRI, EEG, speech, and clinical data. Well-known interpretability tools such as SHapley Additive explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Gradweighted Class Activation Mapping (Grad-CAM), and Layer-wise Relevance Propagation (LRP) have increased the interpretability of deep learning and ensemble models. Similar findings show that CNN-based Grad-CAM methods demonstrated the highest accuracy of 94% and that hybrid and multimodal XAI models based on SHAP and rule-based logic achieved the highest accuracy of over 95 %. Even with these developments, there are still some problems, such as unstable explanation, dependency on datasets, insufficient multimodal integration and absence of standard evaluation measures. The results highlight the need to have clinically validated and interpretable AI systems in order to guarantee healthcare professionals have the ethical ability to deploy AI in real-world settings and guarantee their reliability. In general, XAI-based frameworks symbolize a revolutionary direction to Alzheimer detection, which is transparent and trustworthy.