Alzheimer’s disease (AD) remains a significant global health challenge, affecting millions worldwide and imposing substantial burdens on healthcare systems. Advances in artificial intelligence (AI), particularly in deep learning and machine learning, have revolutionized neuroimaging-based AD diagnosis. However, the complexity and lack of interpretability of these models limit their clinical applicability. Explainable Artificial Intelligence (XAI) addresses this challenge by providing insights into model decision-making, enhancing transparency, and fostering trust in AI-driven diagnostics. This review explores the role of XAI in AD neuroimaging, highlighting key techniques such as SHAP, LIME, Grad-CAM, and Layer-wise Relevance Propagation (LRP). We examine their applications in identifying critical biomarkers, tracking disease progression, and distinguishing AD stages using various imaging modalities, including MRI and PET. Additionally, we discuss current challenges, including dataset limitations, regulatory concerns, and standardization issues, and propose future research directions to improve XAI’s integration into clinical practice. By bridging the gap between AI and clinical interpretability, XAI holds the potential to refine AD diagnostics, personalize treatment strategies, and advance neuroimaging-based research.

: Meningitis and meningoencephalitis, as inflammatory diseases of the brain parenchyma, are serious events reported sporadically after coronavirus disease 2019 (COVID-19) vaccination. However, there is a lack of systematic reviews consolidating these reported cases. By using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science databases. All case reports and series discussing the emergence of meningitis and meningoencephalitis after COVID-19 vaccination were included and evaluated using the Joanna Briggs Institute critical appraisal tool. Out of 967 records, 27 studies with 31 patients were eventually included. The most commonly reported symptoms were headaches and fever. About one-third of the patients exhibited positive meningeal signs. Most of the findings in the computed tomography scans and magnetic resonance images revealed no significant changes or enhancement in the leptomeninges. Cerebrospinal fluid analysis dominantly suggested aseptic meningitis, and about 80.6% of the patients experienced a full recovery. After a detailed review of the reported cases, further research is needed to establish a definitive correlation between meningitis and COVID-19 vaccination on a larger scale.