Explainable artificial intelligence in Alzheimer’s disease classification: a systematic review
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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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SHAP provides unified, quantitative feature importance scores across fundamentally different data types.
“This allows SHAP to provide unified, quantitative feature importance scores across fundamentally different data types”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Viswan et al., 2024
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InfluentialIn-depth insights into Alzheimer’s disease by using explainable machine learning approach
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“The XAI-based AD papers in our study are broadly classified under three main categories: Authors in [125] provided local and global interpretation in the conversion of Dementia to MCI using the XGBoost algorithm.”
InfluentialRecommender Systems: An Explainable AI Perspective
Vultureanu‐Albişi, A., & Bădică, C. (2021) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista52262.2021.9548125 · Cited by 70 · cited 1× · see citing works
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Nahian, M. J. A., Ghosh, T., Uddin, M. N., Islam, M. M., Mahmud, M., & Kaiser, M. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59277-6_25 · Cited by 69 · cited 1× · see citing works
“…disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social…”
Self-citationBloch, L., Friedrich, C. M., & Initiative, F. T. A. D. N. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00879-4 · Cited by 68 · cited 6× · see citing works
“Bloch and Friedrich [123] propose a study that compares the classification using RF and XGBoost for volumetric measurements of MRI scans of control and dementia patients.”
InfluentialExplainable Identification of Dementia From Transcripts Using Transformer Networks
Ilias, L., & Askounis, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172479 · Cited by 67 · cited 5× · see citing works
“Another article by Ilias et al. [131] has used speech The sunburst chart in Fig.”
InfluentialGhosh, T., Banna, M. H. A., Nahian, M. J. A., Uddin, M. N., Kaiser, M., & Mahmud, M. (2022) · Expert systems with applications · doi:10.1016/j.eswa.2022.119007 · Cited by 66 · see citing works
Self-citationSecured Smart Healthcare System: Blockchain and Bayesian Inference Based Approach
Farhin, F., Kaiser, M. S., & Mahmud, M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-4673-4_36 · Cited by 66 · cited 1× · see citing works
“…monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82], etc. Part of these methods has…”
Self-citationWang, D., Honnorat, N., Fox, P. T., Ritter, K., Eickhoff, S. B., Seshadri, S. S., & Habes, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119929 · Cited by 64 · see citing works
DIG: A Turnkey Library for Diving into Graph Deep Learning ResearchPreprint
Liu, M., Luo, Y., Wang, L., Xie, Y., Yuan, H., Gui, S., Yu, H., Zhao, X., Zhang, J., Liu, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12608 · Cited by 62 · see citing works
A Novel AI-Based System for Detection and Severity Prediction of Dementia Using MRI
Jain, V., Nankar, O., Jerrish, D. J., Gite, S., Patil, S., & Kotecha, K. V. (2021) · IEEE Access · doi:10.1109/access.2021.3127394 · Cited by 62 · cited 2× · see citing works
“Jain et al. [160] construct a heatmap emphasising the characteristics discovered from the input MRI scan for each layer of the CNN model using GradCAM.”
Explanation-Driven HCI Model to Examine the Mini-Mental State for Alzheimer’s Disease
Gaur, L., Bhandari, M., Shikhar, B. S., Jhanjhi, N. Z., Shorfuzzaman, M., & Masud, M. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3527174 · Cited by 61 · cited 3× · see citing works
“There have been several noteworthy applications of XAI in AD classification too [120–134] However, an exclusive systematic review article on XAI for AD classification that points to various XAI frameworks and blackbox models used inside these frameworks is yet to be proposed by the research…”
González‐Domínguez, R., Castellano‐Escuder, P., Lefèvre‐Arbogast, S., Low, D. Y., Preez, A. D., Ruigrok, S. R., Lee, H.-A., Helmer, C., Pallàs, M., Urpí-Sardà, M., et al. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00948-8 · Cited by 58 · see citing works
Detection of Breast Cancer from Five-View Thermal Images Using Convolutional Neural Networks
Mammoottil, M. J., Kulangara, L. J., Cherian, A., Mohandas, P., Hasikin, K., & Mahmud, M. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4295221 · Cited by 57
Self-citationAutomatic Lung Health Screening Using Respiratory Sounds
Mukherjee, H., Sreerama, P., Dhar, A., Obaidullah, S. M., Roy, K., Mahmud, M., & Santosh, K. (2021) · Journal of medical systems · doi:10.1007/s10916-020-01681-9 · Cited by 57
Self-citationMediating Community-AI Interaction through Situated Explanation
Kou, Y., & Gui, X. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415173 · Cited by 57 · cited 1× · see citing works
“determine what establishes a good explanation without vali-38 dating with a professional from the medical domain [175].”
Explainable Multimodal Machine Learning for Engagement Analysis by Continuous Performance Test
Rahman, M. A., Brown, D. J., Shopland, N., Burton, A. M., & Mahmud, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05039-8_28 · Cited by 56 · see citing works
Self-citationMachine Learning in Fighting Pandemics: A COVID-19 Case Study
Mahmud, M., & Kaiser, M. S. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-9682-7_9 · Cited by 54 · see citing works
Self-citationEffect of Corpora on Classification of Fake News using Naive Bayes Classifier
Adiba, F. I., Islam, T., Kaiser, M., Mahmud, M., & Rahman, M. A. (2020) · International Journal of Automation, Artificial Intelligence and Machine Learning · doi:10.61797/ijaaiml.v1i1.45 · Cited by 50 · cited 1×
“…security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82], etc. Part of these methods has significantly boosted clinical diagnosis of AD…”
Self-citationRuengchaijatuporn, N., Chatnuntawech, I., Teerapittayanon, S., Sriswasdi, S., Itthipuripat, S., Hemrungrojn, S., Bunyabukkana, P., Petchlorlian, A., Chunamchai, S., Chotibut, T., et al. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-022-01043-2 · Cited by 49 · cited 5× · see citing works
“Ruengchaijatuporn et al. [124] use images of bedside tasks like clock drawing tests, cube-copying and tail-making tests to classify between HC and AD patients in a deep neural network.”
InfluentialACCU3RATE: A mobile health application rating scale based on user reviews
Biswas, M., Tania, M. H., Kaiser, M., Kabir, R., Mahmud, M., & Kemal, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258050 · Cited by 48 · cited 1× · see citing works
“…monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82], etc. Part of these methods has significantly…”
Self-citationOh, K., Yoon, J., & Suk, H.-I. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3197845 · Cited by 48 · see citing works
Alzheimer's Disease Analysis using Explainable Artificial Intelligence (XAI)
Sudar, K., Nagaraj, P., Nithisaa, S., Aishwarya, R., Aakash, M., & Lakshmi, S. I. (2022) · 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds53736.2022.9760858 · Cited by 47 · see citing works
fASSERT: A Fuzzy Assistive System for Children with Autism Using Internet of Things
Sumi, A. I., Zohora, M. F., Mahjabeen, M., Faria, T. J., Mahmud, M., & Kaiser, M. S. (2018) · BI · doi:10.1007/978-3-030-05587-5_38 · Cited by 47 · cited 1× · see citing works
“…anomaly detection [7–9], biosignal and image analysis [10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly…”
Self-citationExplainable AI in Industry: Practical Challenges and Lessons Learned
Gade, K., Geyik, S., Kenthapadi, K., Mithal, V., & Taly, A. (2020) · Companion Proceedings of the Web Conference 2020 · doi:10.1145/3366424.3383110 · Cited by 46 · see citing works
A novel explainable neural network for Alzheimer's disease diagnosis
Yu, L., Xiang, W., Fang, J., Chen, Y.-P. P., & Zhu, R. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108876 · Cited by 44 · see citing works
Towards a data-driven adaptive anomaly detection system for human activity
Yahaya, S., Lotfi, A., & Mahmud, M. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.02.006 · Cited by 44 · see citing works
Self-citationDeepa, B., Murugappan, M., Sumithra, M., Mahmud, M., & Al-Rakhami, M. S. (2022) · IEEE Access · doi:10.1109/access.2021.3100549 · Cited by 44 · see citing works
Self-citationPrakash, N. B., Murugappan, M., Hemalakshmi, G., Jayalakshmi, M., & Mahmud, M. (2021) · Sustainable cities and society · doi:10.1016/j.scs.2021.103252 · Cited by 42 · see citing works
Self-citationNahiduzzaman, M., Tasnim, M., Newaz, N. T., Kaiser, M., & Mahmud, M. (2020) · BI · doi:10.1007/978-3-030-59277-6_19 · Cited by 41
Self-citationHernández, M., Ramon-Julvez, U., & Ferraz, F. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264695 · Cited by 40 · see citing works
Explainable AI for Cheating Detection and Churn Prediction in Online Games
Tao, J., Xiong, Y., Zhao, S.-W., Wu, R.-Z., Shen, X.-D., Lyu, T., Fan, C.-J., Hu, Z.-P., Zhao, S., & Pan, G. (2023) · IEEE Transactions on Games · doi:10.1109/tg.2022.3173399 · Cited by 40 · cited 1× · see citing works
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Santi, L. A. D., Pasini, E., Santarelli, M., Genovesi, D., & Positano, V. (2022) · Journal of digital imaging · doi:10.1007/s10278-022-00719-3 · Cited by 40 · see citing works
Paul, A., Basu, A., Mahmud, M., Kaiser, M., & Sarkar, R. (2022) · Neural computing & applications (Print) · doi:10.1007/s00521-021-06737-6 · Cited by 40 · see citing works
Self-citationRough Sets in COVID-19 to Predict Symptomatic Cases
Bhapkar, H., Mahalle, P., Shinde, G., & Mahmud, M. (2020) · COVID-19: Prediction, Decision-Making, and its Impacts · doi:10.1007/978-981-15-9682-7_7 · Cited by 40
Self-citationAn XAI Based Autism Detection: The Context Behind the Detection
Biswas, M., Kaiser, M., Mahmud, M., Mamun, S. A., Hossain, M. S., & Rahman, M. A. (2021) · BI · doi:10.1007/978-3-030-86993-9_40 · Cited by 40 · see citing works
Self-citationChun, M.-Y., Park, C. J., Kim, J.-H., Jeong, J., Jang, H., Kim, K., & Seo, S. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.898940 · Cited by 39 · cited 7× · see citing works
“[126].”
InfluentialExploring Alzheimer's Disease Prediction with XAI in various Neural Network Models
Shad, H. A., Rahman, Q. A., Asad, N. B., Bakshi, A., Mursalin, S. M. F., Reza, M., & Parvez, M. (2021) · IEEE Region 10 Conference · doi:10.1109/tencon54134.2021.9707468 · Cited by 37 · cited 1× · see citing works
“The predictions about the likely outcome of the generated data are visualised using LIME by Shad et al. [128] and Sidulova et al. [157].”
Artificial and Internet of Healthcare Things Based Alzheimer Care During COVID 19
Jesmin, S., Kaiser, M., & Mahmud, M. (2020) · BI · doi:10.1007/978-3-030-59277-6_24 · Cited by 36 · cited 1× · see citing works
“…on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service…”
Self-citationEstimating explainable Alzheimer’s disease likelihood map via clinically-guided prototype learning
Mulyadi, A. W., Jung, W., Oh, K., Yoon, J. S., Lee, K. H., & Suk, H. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120073 · Cited by 36 · see citing works
Kim, M., Kim, J., Qu, J., Huang, H., Long, Q., Sohn, K., Kim, D., & Shen, L. (2021) · IEEE International Conference on Bioinformatics and Biomedicine · doi:10.1109/bibm52615.2021.9669504 · Cited by 34 · cited 3×
“The study proposed by Kim et al. [129] uses an interpretable Graph Neural Network (GNN) for classifying AD and MCI.”
InfluentialShaffi, N., Hajamohideen, F., Mahmud, M., Abdesselam, A., Karthikeyan, S., & Sariri, A. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15037-1_23 · Cited by 34 · see citing works
Self-citationMahmud, M., Kaiser, M., Rahman, M. A., Wadhera, T., Brown, D. J., Shopland, N., Burton, A., Hughes-Roberts, T., Mamun, S. A., Ieracitano, C., et al. (2022) · Interacción · doi:10.1007/978-3-031-05039-8_26 · Cited by 33 · see citing works
Self-citationBloch, L., Friedrich, C. M., & Initiative, A. D. N. (2022) · SN Computer Science · doi:10.1007/s42979-022-01371-y · Cited by 33 · cited 4× · see citing works
“SHAP is used by Salih et al. [159] and Bloch et al. [161] to determine the order of informative predictors in test data.”
InfluentialArrotta, L., Civitarese, G., & Bettini, C. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3517224 · Cited by 32 · see citing works
Galazzo, I. B., Cruciani, F., Brusini, L., Salih, A. M., Radeva, P. I., Storti, S. F., & Menegaz, G. (2022) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3126573 · Cited by 32 · cited 1× · see citing works
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Rahman, M. A., Brown, D. J., Mahmud, M., Harris, M. C., Shopland, N., Heym, N., Sumich, A., Turabee, Z., Standen, B., Downes, D., et al. (2023) · Brain Informatics · doi:10.1186/s40708-023-00193-9 · Cited by 31 · cited 1× · see citing works
“…various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82], etc. Part of these methods has significantly boosted clinical diagnosis of AD in an incredibly accurate, fast,…”
Self-citationFabietti, M. I., Mahmud, M., Lotfi, A., Avarua, A., Gugganmos, D., Xudo, R., & Chiappalone, M. (2020) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn48605.2020.9207320 · Cited by 31 · see citing works
Self-citationEnsemble Classifiers for a 4-Way Classification of Alzheimer’s Disease
Shaffi, N., Hajamohideen, F., Abdesselam, A., Mahmud, M., & Karthikeyan, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-24801-6_16 · Cited by 31 · see citing works
Self-citationAkhund, T. M. N. U., Mahi, M., Tanvir, A., Mahmud, M., & Kaiser, M. (2018) · BI · doi:10.1007/978-3-030-05587-5_39 · Cited by 29 · cited 1× · see citing works
“…[10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust…”
Self-citationTasnim, N., Mamun, S. A., Islam, M. S., Kaiser, M. S., & Mahmud, M. (2023) · Applied Sciences · doi:10.3390/app13106138 · Cited by 29 · cited 1× · see citing works
“…of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82],…”
Self-citationWadhera, T., & Mahmud, M. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3232550 · Cited by 29 · see citing works
Self-citationA Flexible Keyphrase Extraction Technique for Academic Literature
Rabby, G., Azad, S., Mahmud, M., Zamli, K. Z., & Rahman, M. M. (2018) · International Conference on Computer Science and Computational Intelligence · doi:10.1016/j.procs.2018.08.208 · Cited by 28 · see citing works
Self-citationFabietti, M. I., Mahmud, M., & Lotfi, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59277-6_14 · Cited by 28 · see citing works
Self-citationLai, Y., Lin, X., Lin, C., Lin, X., Chen, Z., & Zhang, L. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.975774 · Cited by 27 · cited 3× · see citing works
“It impedes testing of the model on an extensive dataset which is crucial in determining the actual robustness [131, 155, 156, 179].”
Forecasting the Risk of Type II Diabetes using Reinforcement Learning
Zohora, M. F., Tania, M. H., Kaiser, M. S., & Mahmud, M. (2020) · International Conference on Informatics, Electronics and Vision · doi:10.1109/icievicivpr48672.2020.9306653 · Cited by 27 · cited 1× · see citing works
“…of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81,…”
Self-citationGarcía-Gutierrez, F., Díaz-Álvarez, J., Matias-Guiu, J., Pytel, V., Matías-Guíu, J., Cabrera-Martín, M., & Ayala, J. L. (2022) · Medical and Biological Engineering and Computing · doi:10.1007/s11517-022-02630-z · Cited by 25 · cited 1× · see citing works
“In another study, García-Gutierrez et al. [162] proposed a diagnostic tool that uses a DT that provides a simple and unambiguous set of decision rules to provide capabilities to clinicians to give insights into the pathophysiology of AD and behavioural Fronto Temporal Dementia (bvFTD).”
Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network
Farhin, F., Sultana, I., Islam, N., Kaiser, M. S., Rahman, M. S., & Mahmud, M. (2020) · International Conference on Informatics, Electronics and Vision · doi:10.1109/icievicivpr48672.2020.9306666 · Cited by 25 · see citing works
Self-citationGetting Playful with Explainable AI: Games with a Purpose to Improve Human Understanding of AI
Fulton, L. B., Wang, Q., Hammer, J., Lee, J. Y., Yuan, Z., & Perer, A. (2020) · CHI Extended Abstracts · doi:10.1145/3334480.3382831 · Cited by 25 · cited 1× · see citing works
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia
Chen, T., Su, P., Shen, Y., Chen, L., Mahmud, M., Zhao, Y., & Antoniou, G. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.867664 · Cited by 24 · see citing works
Self-citationAhuja, N. J., Thapliyal, M., Bisht, A., Stephan, T., Kannan, R., Al-Rakhami, M. S., & Mahmud, M. (2022) · IEEE Access · doi:10.1109/access.2021.3115409 · Cited by 24 · cited 1× · see citing works
“…[57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82], etc. Part of these methods has significantly boosted clinical diagnosis of AD in an incredibly accurate, fast,…”
Self-citationIndoor Navigation Support System for Patients with Neurodegenerative Diseases
Biswas, M., Rahman, A. F. M. M., Kaiser, M., Mamun, S., Mizan, K., Islam, M. S., & Mahmud, M. (2021) · BI · doi:10.1007/978-3-030-86993-9_37 · Cited by 24
Self-citationMellem, M. S., Kollada, M., Tiller, J., & Lauritzen, T. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01510-0 · Cited by 23 · cited 1× · see citing works
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Towards Secured Service Provisioning for the Internet of Healthcare Things
Farhin, F., Kaiser, M. S., & Mahmud, M. (2020) · Advanced Industrial Conference on Telecommunications · doi:10.1109/aict50176.2020.9368580 · Cited by 23 · cited 1× · see citing works
“…supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning…”
Self-citationNeurorobots as a Means Toward Neuroethology and Explainable AI
Chen, K., Hwu, T. J., Kashyap, H. J., Krichmar, J. L., Stewart, K. M., Xing, J., & Zou, X. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.570308 · Cited by 22 · cited 1× · see citing works
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Bordin, V., Coluzzi, D., Rivolta, M., & Baselli, G. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc48229.2022.9871306 · Cited by 21 · cited 4× · see citing works
“Another closely related issue with the dataset is the issue of class imbalance [123, 165].”
InfluentialAkter, T., Ali, M., Satu, M. S., Khan, M. I., & Mahmud, M. (2021) · BI · doi:10.1007/978-3-030-86993-9_36 · Cited by 21 · see citing works
Self-citationA machine learning pipeline to classify foetal heart rate deceleration with optimal feature set
Das, S., Obaidullah, S. M., Mahmud, M., Kaiser, M. S., Roy, K., Saha, C. K., & Goswami, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-27707-z · Cited by 20 · see citing works
Self-citationFabietti, M. I., Mahmud, M., Lotfi, A., Leparulo, A., Fontana, R., Vassanelli, S., & Fasolato, C. (2023) · IEEE transactions on neural systems and rehabilitation engineering · doi:10.1109/tnsre.2023.3288835 · Cited by 20 · cited 1× · see citing works
“…[10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust…”
Self-citationSMILE: systems metabolomics using interpretable learning and evolution
Sha, C., Čuperlović-Culf, M., & Hu, T. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04209-1 · Cited by 20 · cited 3× · see citing works
“Authors in [153] have used a metabolomic dataset to identify the key metabolites and their interaction associated with AD.”
InfluentialWanner, J., Herm, L.-V., Heinrich, K., & Janiesch, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85447-8_22 · Cited by 20 · cited 1× · see citing works
“There is often a trade-off between model accuracy and associated explainability [104].”
Salih, A. M., Galazzo, I. B., Cruciani, F., Brusini, L., & Radeva, P. I. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897253 · Cited by 20 · cited 3× · see citing works
“In another study, Salih et al. [159] try to develop a proxy that will check for stability in the explanation by choosing the proper XAI method, classifier, and available data.”
Lalotra, G. S., Kumar, V., Bhatt, A., Chen, T., & Mahmud, M. (2022) · Secur. Commun. Networks · doi:10.1155/2022/9149164 · Cited by 18
Self-citationWadhera, T., & Mahmud, M. (2022) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn55064.2022.9892799 · Cited by 17 · see citing works
Self-citationRahman, S., Sharma, T., & Mahmud, M. (2020) · BI · doi:10.1007/978-3-030-59277-6_22 · Cited by 17
Self-citationKorda, A., Andreou, C., Rogg, H. V., Avram, M., Ruef, A., Davatzikos, C., Koutsouleris, N., & Borgwardt, S. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02242-z · Cited by 16 · see citing works
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Self-citationA Deep Concatenated Convolutional Neural Network-Based Method to Classify Autism
Wadhera, T., Mahmud, M., & Brown, D. J. (2022) · International Conference on Neural Information Processing · doi:10.1007/978-981-99-1648-1_37 · Cited by 15 · cited 1× · see citing works
“…anomaly detection [7–9], biosignal and image analysis [10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly…”
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Self-citationComprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis
Shafiq, S., Ahmed, S., Kaiser, M., Mahmud, M., Hossain, M. S., & Andersson, K. (2023) · IEEE Access · doi:10.1109/access.2022.3232292 · Cited by 14
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Self-citationContactless Fall Detection for the Elderly
Nahian, M. J. A., Raju, M. H., Tasnim, Z., Mahmud, M., Ahad, M., & Kaiser, M. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-68590-4_8 · Cited by 13 · cited 1× · see citing works
“…detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media…”
Self-citationMotin, M. A., Mahmud, M., & Brown, D. J. (2022) · Advanced Industrial Conference on Telecommunications · doi:10.1109/aict55583.2022.10013589 · Cited by 12 · see citing works
Self-citationWadhera, T., & Mahmud, M. (2022) · BI · doi:10.1007/978-3-031-15037-1_5 · Cited by 12 · see citing works
Self-citationiConDet: An Intelligent Portable Healthcare App for the Detection of Conjunctivitis
Mukherjee, P., Bhattacharyya, I., Mullick, M., Kumar, R., Roy, N., & Mahmud, M. (2021) · Analogical and Inductive Inference · doi:10.1007/978-3-030-82269-9_3 · Cited by 12
Self-citationTrust IoHT: A Trust Management Model for Internet of Healthcare Things
Esha, N. H., Tasmim, M., Huq, S., Mahmud, M., & Kaiser, M. (2020) · doi:10.1007/978-981-15-7561-7_3 · Cited by 12
Self-citationAnomaly Detection in Electroencephalography Signal Using Deep Learning Model
Tahura, S., Samiul, S. M. H., Kaiser, M., & Mahmud, M. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-981-33-4673-4_18 · Cited by 11 · see citing works
Self-citationInterpretability of deep neural networks used for the diagnosis of Alzheimer's disease
Pohl, T., Jakab, M., & Benešová, W. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22657 · Cited by 10 · cited 5× · see citing works
“From Table 7, the articles [101, 121, 130, 158, 159], and [132] propose classifiers of deep neural networks for prediction and classification between HC, MCI and AD.”
InfluentialXu, X., & Yan, X.-Y. (2022) · 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) · doi:10.1109/aeeca55500.2022.9918895 · Cited by 9 · cited 3×
“The study of Xu et al. [152] involves deep learning models that include Random Seeds and Nested Cross-validation, SVM-SMOTE, and RF for a three-way AD classification.”
InfluentialAn Attention-Based Mood Controlling Framework for Social Media Users
Ghosh, T., Banna, M. H. A., Angona, T. M., Nahian, M. J. A., Uddin, M. N., Kaiser, M., & Mahmud, M. (2021) · BI · doi:10.1007/978-3-030-86993-9_23 · Cited by 9
Self-citationTowards the Development of an Adaptive System for Detecting Anomaly in Human Activities
Yahaya, S., Lotfi, A., & Mahmud, M. (2020) · IEEE Symposium Series on Computational Intelligence · doi:10.1109/ssci47803.2020.9308415 · Cited by 7 · see citing works
Self-citationMamun, S. A., Kaiser, M., & Mahmud, M. (2021) · BI · doi:10.1007/978-3-030-86993-9_15 · Cited by 7
Self-citationDetection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning
Fabietti, M. I., Mahmud, M., Lotfi, A., Leparulo, A., Fontana, R., Vassanelli, S., & Fassolato, C. (2022) · BI · doi:10.1007/978-3-031-15037-1_3 · Cited by 6
Self-citationSutton, S., Mahmud, M., Singh, R. P., & Yovera, L. (2022) · Analogical and Inductive Inference · doi:10.1007/978-3-031-24801-6_17 · Cited by 3 · cited 1× · see citing works
“…deep learning (DL) algorithms have contributed to diverse application domains including: anomaly detection [7–9], biosignal and image analysis [10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44],…”
Self-citation(2017) · doi:10.9728/dcs.2024.25.7.1847 · Cited by 2 · cited 2× · see citing works
“Some popular model-agnostic approaches are LIME [100] and SHapley Additive exPla-nations (SHAP) [140].”
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What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach
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“From a technical perspective, a distinction can be made between two different forms of explainability methods: ante hoc and post hoc explanation methods [9,23,26,28–30 , 32 Here, a second model is used to provide approximate explanations of a black box model.”
InfluentialML-Powered Handwriting Analysis for Early Detection of Alzheimer’s Disease
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InfluentialExplainable AI for Alzheimer Detection: A Review of Current Methods and Applications
Saif, F. H., Al-Andoli, M. N., & Bejuri, W. M. Y. W. (2024) · Applied Sciences · doi:10.3390/app142210121 · Cited by 43 · see citing works
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning
Erdem, B., & Karabatak, M. (2025) · Applied Sciences · doi:10.3390/app15010400 · Cited by 26 · see citing works
“The term model-independent means that the explanatory algorithm can explain any model by treating the original model as a black-box model [48].”
Ki, M., Kim, D. H., Abdelhamid, M. A. A., & Pack, S. P. (2025) · Biosensors · doi:10.3390/bios15110737 · Cited by 29 · see citing works
Pendyala, V. S., & Kim, H. (2024) · Electronics · doi:10.3390/electronics13061025 · Cited by 24 · see citing works
“In yet another recent survey on XAI methods, including LIME and SHAP, for use in the detection of Alzheimer’s disease [20], the authors also highlighted the limitations and open challenges with XAI methods.”
MethodA Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging
Çetin-Kaya, Y., & Kaya, M. (2024) · Diagnostics · doi:10.3390/diagnostics14040383 · Cited by 24 · see citing works
From AI to the Era of Explainable AI in Healthcare 5.0: Current State and Future Outlook
Rahman, A., Kundu, D., Debnath, T., Rahman, M., Das, U. K., Miah, A. S. M., & Muhammad, G. (2025) · Expert Systems · doi:10.1111/exsy.70060 · Cited by 27 · see citing works
Ali, S. S. A., Memon, K., Yahya, N., & Khan, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11146-5 · Cited by 24 · see citing works
“The unavailability of such multi-modal data for multiple neurological disorders is the major challenge in CAD research in this domain, since the models trained using small datasets result in generalizability issues rendering them unusable in routine clinical practice (Viswan et al. 2024 ).”
Alsubai, S., Ojo, S., Nathaniel, T. I., Ayari, M., Baili, J., Almadhor, A. S., & Hejaili, A. A. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1618550 · Cited by 23 · see citing works
Alahmed, H. A., & Al-Suhail, G. A. (2025) · Journal of Supercomputing · doi:10.1007/s11227-025-06924-5 · Cited by 20
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“This approach represents a significant limitation in current research endeavors [62].”
Fabietti, M. I., Mahmud, M., Lotfi, A., Leparulo, A., Fontana, R., Vassanelli, S., & Fasolato, C. (2023) · IEEE transactions on neural systems and rehabilitation engineering · doi:10.1109/tnsre.2023.3288835 · Cited by 20 · see citing works
“Their design was inspired by the biological counterpart, and they allow for the non-linear processing of information [12].”
BackgroundSelf-citationZhang, M., Pan, Y., Cui, Q.-S., Lu, Y., & Yu, W.-H. (2025) · Experimental Gerontology · doi:10.1016/j.exger.2025.112812 · Cited by 19 · see citing works
Viswan, V., Shaffi, N., Malathy, E., Selvi, G. C., Kavitha, B. R., Abdesselam, A., Wang, S., Suganthan, P. N., Shezawi, I. A., & Mahmud, M. (2026) · Brain Informatics · doi:10.1186/s40708-025-00291-w · Cited by 17 · see citing works
Self-citationQuantum AI for Alzheimer's disease early screening
Cappiello, G., & Caruso, F. (2024) · Neurocomputing · doi:10.48550/arxiv.2405.00755 · Cited by 16
“The paper is organized as follows.”
Grigas, O., Maskeliūnas, R., & Damaševičius, R. (2024) · Health technology · doi:10.1007/s12553-024-00823-0 · Cited by 16 · see citing works
A hybrid transformer-based approach for early detection of Alzheimer’s disease using MRI images
Wu, Q., Wang, Y.-N., Zhang, X.-J., Zhang, H.-Q., & Che, K. (2025) · BioImpacts · doi:10.34172/bi.30849 · Cited by 15
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“Several studies have highlighted the di ffi culties that prevent using DLADS on medical images due to the current challenges of XAI methods (Singh et al., 2020, Van der Velden et al., 2022, Viswan et al., 2024).”
BackgroundL., D. S. H., Thomas, S. M., & Kamath, S. S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00529 · Cited by 15 · see citing works
“Viswan et al. [17] investigated visual explanation meth-ods for convolutional neural networks (CNNs) and evaluated techniques such as Grad-CAM and Ablation-CAM.”
MethodBeyond automation: Unveiling the potential of agentic intelligence
Tirulo, A., Yadav, M., Lolamo, M., Chauhan, S., Siano, P., & Shafie‐khah, M. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116218 · Cited by 16 · see citing works
Application of Explainable Artificial Intelligence in Autism Spectrum Disorder Detection
Viswan, V., Shaffi, N., Sadiq, M. A. K., Sirasanagandla, S. R., Aradhya, V. N. M., Kaiser, M. S., Wang, S., & Mahmud, M. (2025) · Cognitive Computation · doi:10.1007/s12559-025-10462-w · Cited by 14 · see citing works
Self-citationExplainable AI-based method for brain abnormality diagnostics using MRI
Hosny, M., Elshenhab, A. M., & Maged, A. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107184 · Cited by 13 · see citing works
Advanced EEG-Based Classification of Alzheimer's Disease Using CNN-LSTM-Attention Architecture
Bunterngchit, C., Chearanai, T., & Bunterngchit, Y. (2024) · 2024 22nd International Conference on Research and Education in Mechatronics (REM) · doi:10.1109/rem63063.2024.10735597 · Cited by 13 · see citing works
Neuroxai: Explainable Deep Learning for Eeg-Based Detection of Alzheimer's and Parkinson's Diseases
Bunterngchit, C., Baniata, L. H., & Rasool, A. (2025) · International Conferences on Biological Information and Biomedical Engineering · doi:10.1109/bibe66822.2025.00107 · Cited by 12 · see citing works
“Similarly, neuroimaging and gene expression data have been fused [18] to localize biomarkers through LIME, a transformer-driven multimodal approach integrating MRI, demographic, and cognitive features were characterized [19].”
A hybrid AI method for lung cancer classification using explainable AI techniques
Shivwanshi, R. R., & Nirala, N. (2025) · Physica Medica · doi:10.1016/j.ejmp.2025.104985 · Cited by 12 · see citing works
A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis
Al-bakri, F. H., Bejuri, W. M. Y. W., Al-Andoli, M. N., Ikram, R. R. R., Khor, H. M., Tahir, Z., & Initiative, T. A. D. N. (2025) · Diagnostics · doi:10.3390/diagnostics15131642 · Cited by 12 · see citing works
“But many existing methods still lack practical explainability for clinical applications [5,17,18].”
Advancing Alzheimer's Therapy: Computational strategies and treatment innovations
Paul, J. K., Malik, A., Azmal, M., Gulzar, T., Afghan, M. T. R., Talukder, O. F., Shahzadi, S., & Ghosh, A. (2025) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2025.02.002 · Cited by 10 · see citing works
Al-bakri, F. H., Bejuri, W. M. Y. W., Al-Andoli, M. N., Ikram, R. R. R., Khor, H. M., Sholva, Y., Ariffin, U. K., Yaacob, N. M., Abas, Z. A., Abidin, Z. Z., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15162060 · Cited by 8 · see citing works
“By reviewing [35], our model contributes to addressing several gaps and criticisms raised by this reference.”
InfluentialLee, G.-B., Jeong, Y.-J., Kang, D., Yun, H.-J., & Yoon, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0315809 · Cited by 9 · see citing works
Nguyen, Q.-T., Le, L., Tran, X.-T., Do, T., & Lin, C. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3678998 · Cited by 5 · see citing works
“In particular, there is a research gap in the evaluation of current XAI methods applied in early AD detection using these techniques [11].”
An Explainable AI Approach to Speech-Based Alzheimer's Dementia Screening
Iqbal, F., Syed, Z. S., Syed, M. S. S., & Syed, A. (2024) · SMM24, Workshop on Speech, Music and Mind 2024 · doi:10.21437/smm.2024-3 · Cited by 8
“Previous studies [7], [8] have demonstrated that incorporating explainable models and handcrafted linguistic features enhances the interpretability and effectiveness of AD screening systems.”
An eXplainable Artificial Intelligence Methodology on Big Data Architecture
Gatta, V. L., Moscato, V., Postiglione, M., & Sperlí, G. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10272-6 · Cited by 8 · see citing works
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Gharaibeh, N. Y. (2025) · Applied Computer Science · doi:10.35784/acs_7375 · Cited by 7 · see citing works
“SHAP provides insights into the contributions of individual features to model predictions, enabling clinicians to understand the MRI data that led to a particular diagnosis (Viswan et al., 2023).”
Slimi, H., Cherif, I., Abid, S., & Sayadi, M. (2025) · Frontiers Artif. Intell. · doi:10.3389/frai.2025.1590599 · Cited by 7 · see citing works
“Viswan et al. (2023) proposed a method to convert a standard neural network into a Bayesian neural network and estimate the variability of predictions by sampling different networks similar to the original one at each forward pass, enabling uncertainty estimation in neural networks.”
Varghese, T., K., S. M., Somasundaram, S., Shilen, N., & Basheer, F. (2024) · 2024 International Conference on Brain Computer Interface & Healthcare Technologies (iCon-BCIHT) · doi:10.1109/icon-bciht63907.2024.10882392 · Cited by 7
“[5].”
Understanding Feature Importance of Prediction Models Based on Lung Cancer Primary Care Data
Rai, T., Shen, Y., He, J., Mahmud, M., Brown, D. J., Kaur, J., O'Dowd, E., Baldwin, D. R., & Hubbard, R. (2024) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn60899.2024.10650819 · Cited by 7 · see citing works
Self-citationUllah, W., Dai, Q., Zulqarnain, R. M., & Fiidow, M. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-43707-1 · Cited by 6 · see citing works
Alzheimer’s disease prediction via an explainable CNN using genetic algorithm and SHAP values
Zahedipour, M., Abadeh, M. S., & Shojaei, S. (2026) · PLoS ONE · doi:10.1371/journal.pone.0337800 · Cited by 6 · see citing works
“A recent systematic review of XAI in Alzheimer’s disease classification analyzed 37 studies, covering frameworks such as LIME, SHAP, GradCAM, and Layer-wise Relevance Propagation (LRP) [36].”
Advancements and challenges in using AI for biomarker detection in early Alzheimer’s disease
Beheshti, I., Albensi, B. C., Freitas, A. A., & Ghafourian, T. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104415 · Cited by 9 · see citing works
Assessing the Interpretability of Machine Learning Models in Early Detection of Alzheimer's Disease
Haddada, K., Khedher, M. I., Jemai, O., Khedher, S. I., & El-Yacoubi, M. A. (2024) · International Conference on Human System Interaction · doi:10.1109/hsi61632.2024.10613551 · Cited by 6 · see citing works
“However, for these models to be embraced by medical professionals, they must also be understandable and provide clear explanations of their decisions [5].”
BackgroundGamified AI Approch for Early Detection of Dementia
Maji, P. K., Acharya, S., Paul, P., Chakraborty, S., & Basu, S. (2024) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2024.109901 · Cited by 6 · see citing works
Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease
Guillén, P., & Frias-Martinez, E. (2026) · Computers, Materials & Continua · doi:10.32604/cmc.2026.076400 · Cited by 5
EEG-based Alzheimer’s disease detection: Past, present, and future
Alidadi, M., Hamavar, R., Abbasi, E., Nabavifard, S., Mohebbi, M., & Asl, B. M. (2025) · Measurement · doi:10.1016/j.measurement.2025.120096 · Cited by 4 · see citing works
Detection and Classification of Alzheimer’s Disease Using Deep and Machine Learning
Khalid, M., Iqbal, N., Ali, B., Rahman, J. S. U., Iqbal, S., Almudaimeegh, L., Hamd, Z. Y., & Gareeballah, A. (2025) · Tomography · doi:10.3390/tomography12010004 · Cited by 6 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“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., Petrou, V., Papageorgiou, P. S., Rivera, L., Gonzalez, C. M., Rivera, G., Papageorgiou, S., & Vassiliou, E. K. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14207360 · Cited by 4 · see citing works
“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 · see citing works
“…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 · see citing works
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 13 · see citing works
Nami, M., Peebles, D., Thabtah, F. A., & 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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
“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 4 · see citing works
Balaji, N., Sathya, V., Sirenjeevi, P., & Sarveshwaran, V. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110550 · Cited by 2
Laxmikantha, K., & Ashokkumar, P. S. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.18269 · Cited by 3 · see citing works
“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
“…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 · see citing works
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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“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 · see citing works
“…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 · doi:10.1109/codit62066.2024.10708434 · Cited by 2 · see citing works
“…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 2 · see citing works
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 · see citing works
Gupta, D., & Chaudhuri, A. (2026) · Journal of Mental Health and Human Behaviour · doi:10.4103/jmhhb.jmhhb_125_25 · Cited by 1 · see citing works
Cekderi, A. B., Guo, S.-L., & Han, L.-N. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104369 · Cited by 1
- 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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