The DOI matches its Crossref record; 161 of the 184 references it cites were found in a registry or an index.
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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Items 101–169 of 169
The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014–2024)
Chaki, J., & Deshpande, G. (2026) · Neuroinformatics · doi:10.1007/s12021-026-09775-4
“The incorporation of ‘Explainable AI’ techniques is also important to mitigate the ‘black box’ effect of DL models and to gain the confidence of clinicians (Farahani et al., 2022; Viswan, et al., 2024).”
Gunawan, R. A., Jondri, & Indwiarti (2025) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis68501.2025.11326972 · Cited by 1
Kalluvila, A., Yan, L.-R., & Carr, J. (2025) · Journal of Medical Imaging and Interventional Radiology · doi:10.1007/s44326-025-00088-5 · Cited by 1
Systematic review of explainable AI in Alzheimer’s diagnosis
Durgham, B. A., Alawida, M., & Al-Rajab, M. M. (2025) · International Journal of Innovative Research and Scientific Studies · doi:10.53894/ijirss.v8i8.10536 · Cited by 2
“These were commonly explained using SHAP, LIME, or ICE. Studies in this category include [6, 15, 21-23, 25, 41].”
InfluentialChen, X., Chen, L., Yao, W., Zuo, Q., Li, Y., Liang, D., Wang, S., Wang, M., & Sun, T. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121510
Wilkins, J., Neira, C., Su, L., & Initiative, A. D. N. (2025) · Brain and Behavior · doi:10.1002/brb3.70893 · Cited by 1
Mostafa, F., Hossain, K., & Khan, H. (2025) · AppliedMath · doi:10.48550/arxiv.2510.00048 · Cited by 1
“The ”black-box” character of AI models impedes clinical application, according to a thorough review of AD detection research employing XAI by Viswan et al. [42].”
Understanding Dementia Speech Alignment with Diffusion-Based Image GenerationPreprint
Mansi, Λεπίπας, Α., Woszczyk, D. C., Guan, Y., & Demetriou, S. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2508.09385
“Model agnostic explainability techniques like SHAP [14] and LIME [15] have been used to identify linguistic patterns [16] as well as key attributes that influence classification outcomes [17, 18].”
Romero, E., González‐González, J., Álvarez-Voces, M., Costa‐Montenegro, E., Díaz-Vázquez, B., Busto-Castiñeira, A., Villar, P., & López‐Romero, L. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1526413 · Cited by 2
Predictive Models for Chronic Cardiac Disease with LIME and SHAP
Baral, S., Satpathy, S., & Satpathy, R. (2024) · 2024 International Conference on Augmented Reality, Intelligent Systems, and Industrial Automation (ARIIA) · doi:10.1109/ariia63345.2024.11051831 · Cited by 1
“Vimbi et al. [7] assessed interaction effect of SHAP measure of an XGBoost-driven model for forecasting gold prices.”
Explainable Deep Learning Architecture for Alzheimer’s Disease Detection using MRI
Parvathavarthini, S., Lakshmi, K., Pyingkodi, M., Sengottayan, S., Danushree, V. S., & Yuthithra, M. (2024) · 2024 5th International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec61587.2024.10722072 · Cited by 1
“Viswan V et al [7] conducted a systematic assessment of research works examining Alzheimer's prediction with XAI over the last ten years, following PRISMA principles.”
BackgroundVisTAD: A Vision Transformer Pipeline for the Classification of Alzheimer’s Disease
Shaffi, N., Viswan, V., & Mahmud, M. (2024) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn60899.2024.10650975 · Cited by 1
“To fully utilize the powerful attention mechanism of VTs and to shift toward the landscape of explainable AI (XAI) [31], our future endeavors aim to explore the interpretability aspect of VTs, specifically focusing on identifying the crucial brain regions that define distinct AD stages.”
BackgroundSelf-citationLuo, Y. (2024) · 2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI) · doi:10.1109/icetci61221.2024.10594041 · Cited by 1
“By building a neural network model, the system can automatically learn the normal operation mode and abnormal behavior mode of the device [15-16].”
BackgroundIntelligent explainable optical sensing on Internet of nanorobots for disease detection
Mesgaribarzi, N., Djenouri, Y., Belbachir, A., Michalak, T. P., & Srivastava, G. (2024) · Nanotechnology Reviews · doi:10.1515/ntrev-2024-0019 · Cited by 1
Multi-modal medical image diagnosis through LIME with explainable AI
Narayanan, P., Ganesh, S., Periasamy, K., Rajendran, S., & Sethuraj, D. P. (2026) · AIP Conference Proceedings · doi:10.1063/5.0298329 · Cited by 1
Licciardi, A., Fiannaca, A., Rosa, M. L., Urso, A. M., & Paglia, L. L. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.180 · Cited by 3
Rani, S., Goel, R., Gupta, K., & Maini, R. (2026) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2026.116149
Zhang, Y.-F., Kuang, W.-T., Li, Y.-L., Cheng, Y.-Q., Wu, K.-S., & Zou, Y.-P. (2026) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-026-00249-9
Hemavathi, U., Durai, S., & Kumar, V. D. (2026) · Scientific Reports · doi:10.1038/s41598-026-71343-2
“According to the current medical circumstances report (Alzheimer’s Association, 2023), the projected growth of the U.S. AD-affected population is expected to increase from 6.5 million to 13.8 million by 2060 5 .”
InfluentialOmidi, M., Bacsu, J.-D., Absher, J. R., Babaieasl, F., Kooshiar, H., Imani, S. M., Ebrahimi, N., Smith, M. L., & Rahemi, Z. (2026) · Bioengineering · doi:10.3390/bioengineering13101225
“Although attention maps highlight influential words or image regions, they do not provide causal explanations and may mislead users about interpretability [116,117].”
Urooj, B., Guruge, D. B., & Gyasi-Agyei, A. (2026) · Data and Information Management · doi:10.1016/j.dim.2026.100144
Tigga, N. P., Kumari, N., & Alnajjar, F. (2026) · Scientific Reports · doi:10.1038/s41598-026-70929-0
Explainable machine learning for Alzheimer's disease characterization using small-sample EEG data
Shen, L., Tong, W., Zhao, Y., Wu, B., Zhang, P., & Kan, J. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1900857
Christybai, P., & Priya, R. (2026) · 2026 4th International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici69824.2026.11723481
“To adopt AI systems into clinical routine, healthcare professionals need clear and understandable information about the factors that impact the accuracy of a model's predictions[7].”
Privacy‐preserving multi‐agent systems for human‐centred inclusive healthcare AI
Mahmud, M., Shaffi, N., Kaiser, M., Rahman, M. A., Rahman, M. M., Viswan, V., Sharmeen, T., Bhattacharya, S., Ahuja, N. J., Ulgen, A., et al. (2026) · The AI Magazine · doi:10.1002/aaai.70087
Self-citationLightweight Neuro-Symbolic Fuzzy Framework for Interpretable Dementia Staging from MRI
Bethalam, V. S. S. G., Poola, R. G., Chodavarapu, T., Galimutti, R., Vaddi, R., & Yellampalli, S. S. (2026) · International Conference on Emerging Techniques in Computational Intelligence (ICETCI) · doi:10.1109/icetci71772.2026.11710516
Padmashali, A. J., Prabhu, A. U., & Koppad, S. (2026) · 2026 International Conference on Emerging in Computing Technologies (INCECT) · doi:10.1109/incect72370.2026.11742932
Enhancing the Explainability of the MRI-Based Brain Tumour Detection with Image Preprocessing
Ismailov, A., Zheleva, I., Georgieva, P., & Hristov, V. (2026) · Applied Sciences · doi:10.3390/app16147307
Dhivyabharathi, V. R., & Kumar, S. S. (2026) · Health Information Science and Systems · doi:10.1007/s13755-026-00465-8
Rajalakshmi, S., N, D., & T, R. (2026) · 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT) · doi:10.1109/icicit69063.2026.11633939
“The importance of Alzheimer’s disease classification interpretation is rising, and systematic reviews have indicated SHAP and Grad-CAM as significant explanations in Alzheimer’s disease classification [10].”
Yogasrinithi, P., Vaishnavi, P., & Elakiya, E. (2026) · 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss69635.2026.11645715
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Explainable AI based Multi-Organ Diagnosis using ROIs and Augmented Reality Visualization
Ghadekar, P., Desai, R., Sadawrate, A., Varpe, R., Shaikh, A., & Khilari, S. (2026) · International Conference Intelligent Data Communication Technologies and Internet Things · doi:10.1109/icici68773.2026.11581070
“Regional approaches using volumetric or thickness measures consistently identify medial temporal and hippocampal atrophy as the most discriminative markers for AD versus cognitively normal cases [1], [3].”
Explainable Multimodal Deep Learning for Early Dementia Detection using MRI and Clinical Data
R, S., & P., K. (2026) · 2026 7th International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca69024.2026.11570618
“Problems with model generalizability to different individuals were found in an evaluation of deep learning methods for predicting the transition from moderate cognitive impairment to Alzheimer's disease [21].”
Barani, M. J., García-Díaz, V., & Núñez‐Valdéz, E. R. (2026) · International Conference on Federated Learning and Intelligent Computing Systems (FLICS) · doi:10.1109/flics70075.2026.11621957
“The main focus of our method is on explanation stability as a critical property necessary for reliable medical AI [11], [15], [16].”
Cross-Model Interpretability of Alzheimer’s Disease Classification Using ADNI Dataset
Abdulsalam, G., Ahmed, M., & Mahmud, M. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11730051
“Explainable AI methods, particularly SHAP, provide unified local and global explanations for linear and tree-based models [7], [8], but existing AD studies rarely assess robustness across models or relate explanations to feature correlation structure [9].”
Self-citationA GAN-Based MRI Denoising Model for Alzheimer’s Disease Using Free Attention and Wasserstein Loss
Amrustian, M. A., & Mahmud, M. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728585
“Currently, a significant paradigm shift toward Explainable AI (XAI) emphasises clinical transparency, leveraging LIME and SHAP to ensure the interpretability of diagnostic models [39], [40].”
Self-citationBridging Expertise and Data: Multi-Label Disease Detection via Causal Learning and Decision Fusion
Zhang, X., Zhang, M.-H., Liu, J.-Q., Yu, Z.-W., & Guo, B. (2026) · Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems · doi:10.65109/fpsf4954
“In healthcare, AI has been used to diagnose many diseases, including lung diseases [13], heart diseases [11], Alzheimer’s diseases [16], diabetes [4], and retinal diseases [3].”
Addressing Class Imbalance in MRI-based Alzheimer’s Diagnosis using WGAN-GP
Desai, J., George, A., Desai, A., & Mahto, R. (2026) · 2026 IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot68874.2026.11569633
“According to a report published in the World Alzheimer Report, around 55 million people worldwide are diagnosed with AD, and this number is expected to reach 139 million by 2050 [1], [2].”
Genetic Algorithm Based Deep Learning System for Alzheimer’s Disease Classification
Palanisamy, G. (2026) · International Journal for Sciences and Technology · doi:10.71097/ijsat.v17.i2.11098
“Vimbi Viswan [4] further reviewed explainableAI (XAI) approaches in Alzheimer’s classification, underlining the necessity of interpretability in medical decision-making systems.”
Zholshybek, N., Abdurakhmanova, E., Bimakhan, A., Jumadilova, D., Baiturlin, Z., Almazan, J., & Bolla, S. (2026) · Frontiers Neuroinformatics · doi:10.3389/fninf.2026.1821249
Martins, R., Cao, Y., & Wu, J. (2026) · Journal of Alzheimer s Disease Reports · doi:10.1177/25424823261449503
Enhancing Transparency in Alzheimer's Disease Diagnosis Through LIME-Based Explainable AI Models
Mehta, V., Maram, B., Poonam, Arora, S., Kaur, H., & Garg, P. (2026) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom70271.2026.11526416
“LIME avoids this through the offering of brief, feature-based explanations without affecting prediction performance [7].”
de Avila-Armenta, E., Hussain, S., Toscano-Martínez, J. H., Garza-Abdala, J. A., Rosales-Gurmendi, D. S. M., Fumagal-González, G., Pedro-Pérez, A. A., & Tamez-Peña, J. G. (2026) · Medical Imaging · doi:10.1117/12.3081037
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Surface Fire Test Using Diffusion-Augmented Multimodal Deep Belief Learning
Aurnob, A. R., Tanim, S. A., Biswas, T., Mridha, M. F., Safran, M. S., Alfarhood, S., & Che, D.-R. (2026) · Fire technology · doi:10.1007/s10694-026-01881-8
Putta, N. R., A, S., B, V., Smitha Vas, P., B, S., & V., K. (2026) · International Conferences on Information Science and System · doi:10.1109/iciss67859.2026.11454004
“Such systems not only improve classification accuracy rate by providing a scalable framework for large-scale clinical deployment [9].”
Lin, Z.-H., & Woo, J.-C. (2026) · Educational Gerontology · doi:10.1080/03601277.2026.2617493
Shades of Uncertainty: How AI Uncertainty Visualizations Affect Trust in Alzheimer's PredictionsPreprint
Reyes, J., Massoumi, M., Batmaz, A. U., & Kersten‐Oertel, M. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2602.01264
“Recent systematic reviews of explainable AI (XAI) in AD research highlight that models often rely on attribution-and occlusion-based explanation techniques, as well as interpretable model architectures, to expose aspects of their decision logic [20, 67].”
Interpretability of Models for Alzheimer's Disease Identification from Speech Information
Lv, T., & Hou, M. (2025) · 2025 5th International Symposium on Artificial Intelligence and Big Data (AIBDF) · doi:10.1109/aibdf67964.2025.11440865
“Current XAI research in AD detection primarily focuses on neuroimaging and biomarkers [5].”
Explainable Ensemble Machine Learning Approach for severity prediction of Hemophilia A
Singh, V. K., Singh, M. P., & Mani, A. (2025) · International Symposium on Embedded Computing and System Design · doi:10.1109/ised67359.2025.11405080
“Comparative studies of LIME and SHAP in areas such as Alzheimer’s disease prediction have demonstrated their complementary power toward global versus local interpretation, and hybrid combinations of LIME and SHAP systems have been used for real-time safety-critical information[14], [15].”
Luxshi, K., & Rathanayake, R. (2025) · 2025 International Conference on Advances in Technology and Computing (ICATC) · doi:10.1109/icatc68823.2025.11407641
“Consequently, there are tremendous effects on daily living, including memory impairment, confusion, mood variations, and eventual incapability to complete basic activities, which not only reduces the quality of life but also imposes huge emotional and financial stress on families and caregivers [4].”
Haque, A. H. M. O., al Fahad, A., Rahman, M. S., & Samee, M. (2025) · bioRxiv · doi:10.1101/2025.11.27.690894
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Alzheimer’s Disease Classification using MRI-based Lightweight CNN–Transformer
Yun, T., Kim, S., Ryu, J.-Y., & Ha, J. (2025) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2025.23.11.181
KE-LPG: Toward Semantic Refinement for Lesson Plan Generation
Guo, H., Zhu, J., Pan, C., Wang, Q., Zhou, C., & Meng, C. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10808-4 · Cited by 1
B, H., J., S., & N., S. (2025) · 2025 International Conference on Communication, Computer, and Information Technology (IC3IT) · doi:10.1109/ic3it66137.2025.11341548
Ali, M. B. G., & Smiti, A. (2025) · IEEE Afro-Mediterranean Conference on Artificial Intelligence (AMCAI) · doi:10.1109/amcai66110.2025.11474378
“Our sub-second latency therefore satisfies recommendations for same-session decision support, where results should be available before the patient leaves the scanner room [58].”
Sağlık Alanında Açıklanabilir Yapay Zekâ Kullanımı Üzerine Bir Uygulama
Toğa, G. (2025) · Çukurova Üniversitesi Mühendislik Fakültesi Dergisi · doi:10.21605/cukurovaumfd.1729986
Sekar, K. T. A., Vs, B., Nandhini, R., M., H., Mellam, M. R., & Kopiya, S. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362565
AI in the Clinic: A Legal and Ethical Framework for Safe and Responsible Deployment
Aditya, R., & Chithambarathanu, M. (2025) · 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11252895
“To analyze EHR data of more than a million of the patients, a wide range of factors were taken into consideration, including such background variables as demographics, medical history, test results, prescription use, lifestyle-related information of smoking status, etc[7].”
Efficient Alzheimer's Disease Classification Using Transfer Learning and Custom CNN Models
Narasimha, S., Manikanta, C., Kamepalli, S., & Keerthan, V. V. (2025) · 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) · doi:10.1109/i2itcon65200.2025.11210736
“By offering reliable and reproducible insights that are difficult to notice through manual examination alone, automated classification algorithms trained on MRI data show potential in helping neurologists [2].”
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Detecting Alzheimer's Disease (Gender-Based) Using Different Machine Learning Approaches
Negi, H. S., Indu, R., Dimri, S. C., Kumar, B., Bisht, N., & Gupta, A. (2025) · International Computer Science Conference · doi:10.1109/icsc64553.2025.10968947 · Cited by 1
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From Cognitive Systems to Alzheimer's Disease: The Role of Computational Modeling
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Alzheimer's Disease: Preemptive Diagnosis With Deep Learning Models
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A Review on Alzheimer Disease Classification using different ML and DL Models
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Deep Learning based Framework for Early-stage Alzheimer Detection
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Biosensors for early detection of neurodegenerative diseases: Analytical challenges and innovations
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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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