ReviewDiagnostics (Basel, Switzerland)2025
Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.
Review in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- A real-world decision tree model based on ¹⁸F-FDG PET/CT Z scores and Mini-Mental State Examination for differentiating mild cognitive impairment from clinically diagnosed Alzheimer disease.Annals of nuclear medicine · 2026Article
- RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.Brain informatics · 2026Review
- Explainable Patient-Level Cognitive Impairment Screening via Temporal, Semantic, and Psycholinguistic Multimodal AI.Journal of Intelligence · 2026Article
- Evaluating Homoscedastic Uncertainty Weighting and Generative Tau Imputation in a Multimodal, Multitask Deep Learning Framework for Alzheimer's Disease.Neuroinformatics · 2026Article
- QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.Scientific reports · 2026Article
- A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's classification.Scientific reports · 2026Article
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.Brain informatics · 2026Review
- Integration of AI diagnostic tools into clinical practice for Alzheimer's disease: barriers and solutions.Annals of medicine and surgery (2012) · 2026Review
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- Lightweight Deep Learning Models with Explainable AI for Early Alzheimer's Detection from Standard MRI Scans.Diagnostics (Basel, Switzerland) · 2025Article
- An Explainable Web-Based Diagnostic System for Alzheimer's Disease Using XRAI and Deep Learning on Brain MRI.Diagnostics (Basel, Switzerland) · 2025Article
- IoMT driven Alzheimer's prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0.Scientific reports · 2025Article
- Baseline Impaired Insight Predicts Longitudinal Brain Atrophy in Alzheimer's Disease and Related Cognitive States: A 30-Month Cohort Study From the ADNI Dataset.Brain and behavior · 2025Article
- Artificial Intelligence in PET Imaging for Alzheimer's Disease: A Narrative Review.Brain sciences · 2025Review
- Data Leakage in Deep Learning for Alzheimer's Disease Diagnosis: A Scoping Review of Methodological Rigor and Performance Inflation.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- A hybrid filtering and deep learning approach for early Alzheimer's disease identification.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.