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Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review
Open accessPublished version of a preprint
Abstract
The unprecedented growth of computational capabilities in recent years has allowed Artificial Intelligence (AI) models to be developed for medical applications with remarkable results. However, a large number of Computer Aided Diagnosis (CAD) methods powered by AI have limited acceptance and adoption in the medical domain due to the typical blackbox nature of these AI models. Therefore, to facilitate the adoption of these AI models among the medical practitioners, the models' predictions must be explainable and interpretable. The emerging field of explainable AI (XAI) aims to justify the trustworthiness of these models' predictions. This work presents a systematic review of the literature reporting Alzheimer's disease (AD) detection using XAI that were communicated during the last decade. Research questions were carefully formulated to categorise AI models into different conceptual approaches (e.g., Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, Global, Local etc.) and frameworks (Local Interpretable Model-Agnostic Explanation or LIME, SHapley Additive exPlanations or SHAP, Gradient-weighted Class Activation Mapping or GradCAM, Layer-wise Relevance Propagation or LRP, etc.) of XAI. This categorisation provides broad coverage of the interpretation spectrum from intrinsic (e.g., Model-Specific, Ante-hoc models) to complex patterns (e.g., Model-Agnostic, Post-hoc models) and by taking local explanations to a global scope. Additionally, different forms of interpretations providing in-depth insight into the factors that support the clinical diagnosis of AD are also discussed. Finally, limitations, needs and open challenges of XAI research are outlined with possible prospects of their usage in AD detection.
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AI-Driven Multimodal Brain-State Decoding for Personalized Closed-Loop TENS: A Comprehensive Review
Du, J., Luo, S., & Shi, P. (2025) · Brain Science · doi:10.3390/brainsci15090903 · Cited by 5
“Techniques such as SHAP (SHapley Additive exPlanations) and layer-wise relevance propagation can enhance model transparency, while hybrid systems that integrate domain knowledge with data-driven models can further improve interpretability without compromising performance [49–53].”
Automatic Alzheimer's Disease Diagnosis Using XGBoost with SHAP for Explainability
Siddiqua, A., Oni, A. M., Miah, A. S. M., Hassan, N., & Shin, J. (2025) · 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN) · doi:10.1109/qpain66474.2025.11172097 · Cited by 5
Interpretable and Explainable Convolutional Neural Network for Rice Leaf Disease Detection
Lipsa, S., Dash, R. K., & Gountia, D. (2025) · SN Computer Science · doi:10.1007/s42979-025-04215-7 · Cited by 5
“The emphasis on explainability in Artificial Intelligence (AI) arises from the necessity to render the neural network’s decision-making procedure transparent and understandable to humans [11, 12].”
Ribino, P., Napoli, C. D., Paragliola, G., Chicco, D., & Gasparini, F. (2025) · BioData Mining · doi:10.1186/s13040-025-00441-0 · Cited by 5
“Other works highlight the growing role of AI for the prediction of early stages and diagnosis of AD [16–18], and the potential of explainable AI [19, 20], to name a few among many.”
Rauf, F., Khan, M. A., Brahim, G. B., Abrar, W., Alasiry, A., Marzougui, M., Jeon, S., & Nam, Y. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1501742 · Cited by 5
Al-bakri, F. H., Bejuri, W. M. Y. W., Al-Andoli, M. N., Ikram, R. R. R., Khor, H. M., Mispan, M. S., Yunos, N. M., Yusof, N. F. A., Fauadi, M. H. F. M., Jaya, A. S. M., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15243118 · Cited by 5
“The ongoing tension between achieving high accuracy and maintaining explain-able interpretations—as identified in [14] further limits the reliability of current methods, especially for general practitioners or non-specialists who require intuitive reasoning and a clinical basis.”
Bharath, H. C., Pradeep, N., Shashidhar, R., & Nanjappa, Y. (2025) · Brain Informatics · doi:10.1186/s40708-025-00280-z · Cited by 4
“SHAP can quantify the role of each feature toward a particular prediction of a diseased class in the case of AD, i.e., in MRI scans a particular pixel will be assigned an importance score, allowing a clinician to know which pixels contribute to a high degree to produce the predictions [12].”
Christodoulou, R. C., Vamvouras, G., Sarquis, M. D., Petrou, V., Papageorgiou, P. S., Rivera, L., Gonzalez, C. M., Rivera, G., Papageorgiou, S. G., & Vassiliou, E. K. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14207360 · Cited by 4
“Because clinical adoption requires transparency, explainable AI (XAI) approaches are favored so clinicians can see which variables drive predictions [21].”
Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia
Tewari, N., Nguyen, L. L. D., Liu, M.-J., Ren, J., Xu, Z.-Q., Sarwar, T., Baths, V., & Xia, F. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3859277 · Cited by 4
“With the emergence of explainability in GNNs [80, 184], there has been growing adoption of XGNNs in diagnosing brain disorders [22], investigating cognitive dysfunctions [114], and studying neurodegenerative diseases such as AD [13, 167] and PD [77].”
Towards a transparent and interpretable AI model for medical image classifications
Wen, B., Wu, Y., Daqqaq, T. S., & Chaddad, A. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10343-w · Cited by 5
“…and accurately reflect the decision and its process [119] Computational efficiency The time for providing explanations are reasonable and fast [120] Domain experts’ input Many researchers may lack medical expertise, leading to errors in assessing the quality of an explanation. the model…”
Baili, J., Alqahtani, A. M., Almadhor, A. S., Hejaili, A. A., & Kim, T.-H. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1562629 · Cited by 4
A Systematic Review of NLP for Dementia: Tasks, Datasets, and Opportunities
Peled-Cohen, L., & Reichart, R. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.35 · Cited by 15
Nami, M., Peebles, D., Thabtah, F., & Kamalov, F. (2026) · Brain Informatics · doi:10.1186/s40708-026-00310-4 · Cited by 3
“Moreover, common visualization techniques, such as saliency maps, are prone to false positives and may also identify regions that are not causally related to the model’s prediction [72].”
Türkiş, F. C. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03585-z · Cited by 3
“Recent reviews and systematic analyses have documented rapid methodological advances and promising predictive performance; however, they have also emphasized persistent challenges related to robustness, reproducibility, and clinical translation [3, 5].”
SHAP-Based Explainability for Local and Global Insights in Alzheimer's Detection
Khanapur, S., Nayak, J. S., Rajeshwari, B., Namratha, M., Bharadwaj, C. B., & Bhardwaj, R. (2026) · Engineering, Technology & Applied Science Research · doi:10.48084/etasr.13932 · Cited by 3
“In [11], an analysis of 89 studies from 2018-2023 revealed that Grad-CAM was the most widely adopted XAI method (42% of studies) for AD detection, followed by SHAP (28”
Petrovska, N., Larrondo-Petrie, M., & Pavlovic, M. (2025) · 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS) · doi:10.1109/amlds63918.2025.11159451 · Cited by 3
“A recent review by Viswan et al. [3] examined how explainability methods like SHAP and LIME are being used in machine learning models for Alzheimer's disease classification.”
Peled-Cohen, L., Zadok, M., Calderon, N., Gonen, H., & Reichart, R. (2025) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.2505.13418 · Cited by 3
Wahid, S. B., Rothy, Z. T., News, R. K., & Rieyan, S. A. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022387 · Cited by 2
Unifying Interpretability and Explainability for Alzheimer's Disease Progression Prediction
Ali, R. F., Milani, S., Woods, J., Adenij, E., Farooq, A., Mansel, C. O., Burns, J., & Hsu, W. (2024) · arXiv.org · doi:10.48550/arxiv.2406.07777 · Cited by 3
“In response to the call for transparency in AI models, there has been a recent surge in research that uses eXplainable AI (XAI) methods for AD diagnosis [7].”
BackgroundMethodViswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2023) · IEEE Symposium Series on Computational Intelligence · doi:10.1109/ssci52147.2023.10371843 · Cited by 3
“We suggest referring to the cited article for a comprehensive understanding of the concepts of interpretability or XAI [17].”
BackgroundSelf-citationSanthosh, A. J. S., Karthik, R., & Suganthi, K. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100821 · Cited by 5
Balaji, N., Sathya, V., Sirenjeevi, P., & Sarveshwaran, V. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110550 · Cited by 1
Laxmikantha, K., & Ashokkumar, P. S. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.18269 · Cited by 3
“However, explainability is mostly used only after, but not during, model training [7, 8].”
Singh, S., Sharma, Y., Bhardwaj, P., Kothari, D., Chhikara, A., Gupta, V., Kumar, D., Choudhary, N., & Kondaveeti, S. B. (2026) · Behavioral and Brain Functions · doi:10.1186/s12993-026-00329-x · Cited by 2
“…while quantitative intuitions are easier to formally measure and include ideas like identity (similarity between situations and outcomes), stability (comparability between instances from the same category and outcomes), or separability (distinct nature between different instances) [35, 99].”
Söylemez, D. Ö., & Doğru, S. A. (2026) · Diagnostics · doi:10.3390/diagnostics16050754 · Cited by 2
Liu, Y.-L., Li, Y., Feng, R., Chen, J., Wang, Y., Chen, Y., Ding, N., Yuan, J., & Ling, Z.-H. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104968
Towards sustainable WWTPs: Explainable modeling and scenario analysis of N₂O emissions
Sadaghat, B., Behzadian, K., & Najjar-Ghabel, S. (2025) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2025.108818 · Cited by 2
Explainable AI in Healthcare: A Systematic Review of Methods for Interpretable Machine Learning
Pant, H., Joshi, G., Rawat, B., Pant, J., & Singh, D. P. (2025) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscn67106.2025.11308443 · Cited by 1
“XAI focuses on designing methods that make AI outputs understandable, offering not only accurate predictions but also clear insights into the decision-making process [2].”
AI-Driven Early Detection of Alzheimer's Disease Through MRI Analysis
Pandey, A., & Aggarwal, D. (2025) · International Conference on Sustainability, Innovation & Technology (ICSIT) · doi:10.1109/icsit65336.2025.11293929 · Cited by 2
“A good set of questions were framed to develop an interesting review article [14].”
Explainable Vision Ensemble for Alzheimer’s Disease Classification using Brain MRI
Bhongade, A., Dubey, Y. K., & Fulzele, P. (2025) · 2025 IEEE 6th India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon66021.2025.11251953 · Cited by 2
“The growing demand for transparent, interpretable AI models in healthcare has led to integration of explainable AI approaches in DL models for AD classification [14].”
Transforming Alzheimer’s diagnosis: ADNet deep learning with explainable AI framework
Singhal, A., Kumari, A., & Srinivas, K. (2025) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-025-04999-9 · Cited by 2
Divers, R. M., Cohen, A. S., Elvevåg, B., Chandler, C., Turner, R. S., Reynolds, B., & Diaz-Asper, C. M. (2025) · The Clinical Neuropsychologist · doi:10.1080/13854046.2025.2519827 · Cited by 3
“The use of AI and machine learning to integrate speech features together has improved accuracy at the cost of explainability and interpretability (Viswan et al., 2024).”
Exploration of Explainable AI with Deep Learning Model for Early Detection of Alzheimer's Disease
Vernekar, S. R., & Selva Kumar, S. (2024) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss64042.2024.10816763 · Cited by 4
“In [4] this study by Viswan et al. analyzed the last decade’s research on Explainable Artificial Intelligence for AD detection.”
Yan, Z., Guo, Q., & Xue, J.-H. (2024) · Clinical Nursing Research · doi:10.1177/10547738241273158 · Cited by 2
“…of multiple variables that influence outcomes in such a complex patient cohort, and this novel approach exemplifies the capability of explainable artificial intelligence (El-Sappagh et al., 2021; Sudar et al., 2022; Vimbi et al., 2023) in elucidating the factors contributing to patient outcomes.”
AI Based Smart Assistant System for Alzheimer Person
R., K., & J, N. (2024) · International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-] · doi:10.1109/icesc60852.2024.10690092 · Cited by 2
“The machine learning and deep learning algorithms [2] employed in various healthcare sections such as disease diagnosis, prediction, drug recovery, speech recognition, anomaly detection, decision making etc. AI-based solutions [3] on the market that focus on providing an active, real-time support…”
MethodPetreska, A., Nikolovski, S., Novotni, G., & Ristevski, B. (2024) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit62066.2024.10708434 · Cited by 5
“…detect AD involves applying this statistical modeling technique to predict the probability that an individual has AD based on one or more variables, well suited for binary classification tasks [23] because it estimates the probability that an individual belongs to a particular class (AD or non-AD).”
MethodExplainable AI for MRI Alzheimer’s disease classification: A comparative analysis
Persson, S., Svenberg, H. E. A., Moscoso, A., Ferreira, D., & Moreno, R. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122043 · Cited by 3
An explainable deep learning model for dental caries detection and segmentation
Brahmi, W., Jdey, I., & Drira, F. (2026) · The Visual Computer · doi:10.1007/s00371-026-04540-9 · Cited by 1
Gupta, D. K., & Chaudhuri, A. (2026) · Journal of Mental Health and Human Behaviour · doi:10.4103/jmhhb.jmhhb_125_25 · Cited by 1
Cekderi, A. B., Guo, S., & Han, L. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104369 · Cited by 1
Metadata
- Authors
- Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F.
- Affiliations
- University of Technology and Applied Sciences-Suhar (OM)
- Nottingham Trent University (GB)
- Corresponding author
- Mahmud, Mufti
- Venue
- Cognitive Computation
- Published
- 2023-11-13
- Volume
- 16
- Issue
- 1
- Pages
- 1-44
- ISSN
- 1866-9956 (ISSN-L)
- 1866-9964
- OpenAlex
- W4388634755
- Cited by
- 195
- References
- 184
- Citations by year
- 2026: 63 · 2025: 80 · 2024: 49 · 2023: 3
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Cognitive Computation (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- Funders
- Ministry of Higher Education, Government of Oman (MoHERI/BFP/UoTAS/01/2021)
- Nottingham Trent University (Strategic Research Theme Award, 2022)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- explainable AI, Alzheimer's disease detection, computer-aided diagnosis, model interpretability, post-hoc explanation, model-agnostic explanation, local and global explanations, lime, SHAP, Grad-CAM, layer-wise relevance propagation, diagnosis
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