Unveiling Explainable AI in Healthcare: Current Trends, Challenges, and Future Directions
Open accessAbstract
This overview investigates the evolution and current landscape of eXplainable Artificial Intelligence (XAI) in healthcare, highlighting its implications for researchers, technology developers, and policymakers. Following the PRISMA protocol, we analyzed 89 publications from January 2000 to June 2024, spanning 19 medical domains, with a focus on Neurology and Cancer as the most studied areas. Various data types are reviewed, including tabular data, medical imaging, and clinical text, offering a comprehensive perspective on XAI applications. Key findings identify significant gaps, such as the limited availability of public datasets, suboptimal data preprocessing techniques, insufficient feature selection and engineering, and the limited utilization of multiple XAI methods. Additionally, the lack of standardized XAI evaluation metrics and practical obstacles in integrating XAI systems into clinical workflows are emphasized. We provide actionable recommendations, including the design of explainability‐centric models, the application of diverse and multiple XAI methods, and the fostering of interdisciplinary collaboration. These strategies aim to guide researchers in building robust AI models, assist technology developers in creating intuitive and user‐friendly AI tools, and inform policymakers in establishing effective regulations. Addressing these gaps will promote the development of transparent, reliable, and user‐centred AI systems in healthcare, ultimately improving decision‐making and patient outcomes.
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Tools designed to optimize computational metrics without attending to clinical, regulatory, and behavioral dimensions reproduce the same failures that have limited XAI adoption to date.
“Tools designed to optimize computational metrics without attending to clinical, regulatory, and behavioral dimensions reproduce the same failures that have limited XAI adoption to date”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Noor et al., 2025
Section 6.3 says that XAI evaluation overlooks user studies and that clinician trust and acceptance require user-centred explanations; the discussion of barriers to clinical integration also identifies workflow compatibility and clinician training as adoption concerns. The paper mentions regulatory requirements, but these passages do not establish that tools focused on computational metrics reproduce the same clinical, regulatory, and behavioral failures that have limited adoption. (LLM, conf=0.81)
Experimental — accuracy not yet measuredThe lack of standardized evaluation metrics and practical obstacles in integrating XAI systems into clinical workflows are the two most consistently emphasized research gaps in the field.
“the lack of standardized evaluation metrics and practical obstacles in integrating XAI systems into clinical workflows are the two most consistently emphasized research gaps in the field”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Noor et al., 2025
The paper’s 'Broader Use of XAI Methods and Reliable Solutions' section identifies both the lack of standardized XAI evaluation metrics and barriers to clinical integration as research gaps; its conclusion also highlights both. It does not establish that these are the two most consistently emphasized gaps in the field, and describes clinical integration as outside the primary focus of its research questions. Page numbers are unavailable in our copy. (LLM, conf=0.86)
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PRISMA-P Group, Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. A. (2015) · Systematic Reviews · doi:10.1186/2046-4053-4-1 · Cited by 26,223 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · see citing works
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works
Artificial Intelligence (AI) applications for COVID-19 pandemic
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.012 · Cited by 1,502 · see citing works
Electronic health records in ambulatory care—a national survey of physicians
DesRoches, C. M., Campbell, E. G., Rao, S. R., Donelan, K., Ferris, T. G., Jha, A., Kaushal, R., Levy, D. E., Rosenbaum, S., Shields, A. E., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0802005 · Cited by 1,111 · see citing works
Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech TestsPreprint
Tsanas, A., Little, M. A., McSharry, P., & Ramig, L. O. (2009) · Nature Precedings · doi:10.1109/tbme.2009.2036000 · Cited by 833 · see citing works
Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107161 · Cited by 734 · see citing works
What do we need to build explainable AI systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636 · see citing works
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629 · see citing works
Mapping the landscape of Artificial Intelligence applications against COVID-19
Aylett-Bullock, J., Luccioni, A. S., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. A. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12162 · Cited by 517 · see citing works
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448 · see citing works
Clinical guidelines: Using clinical guidelines
Feder, G. S., Eccles, M., Grol, R. P. T. M., Griffiths, C., & Grimshaw, J. (1999) · BMJ · doi:10.1136/bmj.318.7185.728 · Cited by 393 · see citing works
Schwalbe, G., & Finzel, B. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00867-8 · Cited by 381 · see citing works
Ali, S., Akhlaq, F., Imran, A. S., Kastrati, Z., Daudpota, S. M., & Moosa, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107555 · Cited by 370 · see citing works
El-Sappagh, S., Alonso, J. M., Islam, S. M. R., Sultan, A. M., & Kwak, K. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-82098-3 · Cited by 363 · see citing works
Wang, K., Tian, J., Zheng, C., Yang, H., Ren, J., Liu, Y., Han, Q., & Zhang, Y. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104813 · Cited by 361 · see citing works
Thorsen-Meyer, H.-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30018-2 · Cited by 336 · see citing works
Wani, N. A., Kumar, R., & Bedi, J. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107879 · Cited by 318 · see citing works
Band, S. S., Yarahmadi, A., Hsu, C.-C., Biyari, M., Sookhak, M., Ameri, R., Dehzangi, A., Chronopoulos, A. T., & Liang, H. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101286 · Cited by 305 · see citing works
Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K.-L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102412 · Cited by 207 · see citing works
A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification
Nigar, N., Umar, M., Shahzad, M. K., Islam, S., & Abalo, D. (2022) · IEEE Access · doi:10.1109/access.2022.3217217 · Cited by 190 · see citing works
Allgaier, J., Mulansky, L., Draelos, R. L., & Pryss, R. C. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102616 · Cited by 190 · see citing works
Explainable Artificial Intelligence-Based Decision Support Systems: A Recent Review
Kostopoulos, G., Davrazos, G., & Kotsiantis, S. B. (2024) · Electronics · doi:10.3390/electronics13142842 · Cited by 188 · see citing works
Zihni, E., Madai, V. I., Livne, M., Galinovic, I., Khalil, A. A., Fiebach, J. B., & Frey, D. (2020) · PLOS ONE · doi:10.1371/journal.pone.0231166 · Cited by 187 · see citing works
Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain
Knapič, S., Malhi, A. K., Saluja, R., & Främling, K. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3030037 · Cited by 179 · see citing works
Health Care Information Technology
Hersh, W. (2004) · JAMA · doi:10.1001/jama.292.18.2273 · Cited by 174 · see citing works
Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP
Alabi, R. O., Elmusrati, M. S., Leivo, I. V. V., Almangush, A., & Mäkitie, A. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-35795-0 · Cited by 164 · see citing works
Xu, Q., Xie, W., Liao, B., Hu, C., Qin, L., Yang, Z., Xiong, H., Yi, L., Zhou, Y., & Luo, A. (2023) · Journal of Healthcare Engineering · doi:10.1155/2023/9919269 · Cited by 162 · see citing works
Hassan, M. R., Islam, M. F., Uddin, M. Z., Ghoshal, G., Hassan, M. M., Huda, S., & Fortino, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.030 · Cited by 147 · see citing works
Clinical Decision Support Systems
Wasylewicz, A. T. M., & Scheepers-Hoeks, A. A.-M. (2018) · Springer eBooks · doi:10.1007/978-3-319-99713-1_11 · Cited by 132 · see citing works
Du, Y., Rafferty, A. R., McAuliffe, F. M., Wei, L., & Mooney, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-05112-2 · Cited by 128 · see citing works
Grad-CAM-Based Explainable Artificial Intelligence Related to Medical Text Processing
Zhang, H., & Ogasawara, K. (2023) · Bioengineering · doi:10.3390/bioengineering10091070 · Cited by 121 · see citing works
Toward explainable AI (XAI) for mental health detection based on language behavior
Kerz, E., Zanwar, S., Qiao, Y., & Wiechmann, D. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1219479 · Cited by 121 · see citing works
Peng, J., Zou, K., Ζhou, M., Teng, Y., Zhu, X., Zhang, F., & Xu, J. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01736-5 · Cited by 109 · see citing works
Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture
Joseph, L. P., Joseph, E. A., & Prasad, R. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106178 · Cited by 108 · see citing works
Barda, A. J., Horvat, C. M., & Hochheiser, H. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01276-x · Cited by 105 · see citing works
Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences
Hatwell, J., Gaber, M. M., & Azad, R. M. A. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01201-2 · Cited by 104 · see citing works
Junaid, M., Ali, S., Eid, F., El–Sappagh, S., & Abuhmed, T. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107495 · Cited by 104 · see citing works
An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data
Kokkotis, C., Giarmatzis, G., Γιαννακού, Ε., Moustakidis, S., Tsatalas, T., Tsiptsios, D., Vadikolias, Κ., & Aggelousis, N. (2022) · Diagnostics · doi:10.3390/diagnostics12102392 · Cited by 103 · see citing works
Mridha, K., Ghimire, S., Shin, J., Aran, A., Uddin, M. M., & Mridha, M. F. (2023) · IEEE Access · doi:10.1109/access.2023.3278273 · Cited by 103 · see citing works
Zou, L., Goh, H. L., Liew, C. J. Y., Quah, J. L., Gu, G. T., Chew, J. J., Kumar, M. P., Ang, C. G. L., & Ta, A. W. A. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3153754 · Cited by 93 · see citing works
Endoscopic Image Classification Based on Explainable Deep Learning
Doniyorjon, M., Madinakhon, R., Muksimova, S., & Cho, Y. I. (2023) · Sensors · doi:10.3390/s23063176 · Cited by 88 · see citing works
Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram
Ganeshkumar, M., Ravi, V., Sowmya, V., Gopalakrishnan, E. A., & Soman, K. P. (2021) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2021.3104751 · Cited by 87 · see citing works
XAI-Based Clinical Decision Support Systems: A Systematic Review
Kim, S. Y., Kim, D. H., Kim, M., Ko, H.-J., & Jeong, O. (2024) · Applied Sciences · doi:10.3390/app14156638 · Cited by 84 · see citing works
Papadopoulos, P., Soflano, M., Chaudy, Y., Adejo, W., & Connolly, T. M. (2022) · Health and Technology · doi:10.1007/s12553-022-00672-9 · Cited by 82 · see citing works
Cremonesi, F., Planat, V., Kalokyri, V., Kondylakis, H., Sanavia, T., Resinas, V. M. M., Singh, B., & Uribe, S. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104338 · Cited by 71 · see citing works
Pawlicki, M., Pawlicka, A., Uccello, F., Szelest, S., D’Antonio, S., Kozik, R., & Choraś, M. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128282 · Cited by 69 · see citing works
AlMohimeed, A., Saad, R. M. A., Mostafa, S., El-Rashidy, N. M., Farrag, S., Gaballah, A., Elaziz, M. A., El–Sappagh, S., & Saleh, H. (2023) · IEEE Access · doi:10.1109/access.2023.3328331 · Cited by 67 · see citing works
Pintelas, E. G., Liaskos, M., Livieris, I. E., Kotsiantis, S. B., & Pintelas, P. (2020) · Journal of Imaging · doi:10.3390/jimaging6060037 · Cited by 66 · see citing works
WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values
Nayebi, A., Tipirneni, S., Reddy, C. K. A., Foreman, B. P., & Subbian, V. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104438 · Cited by 64 · see citing works
A machine learning and explainable artificial intelligence triage-prediction system for COVID-19
Khanna, V., Chadaga, K., Sampathila, N., Prabhu, S., & P., R. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100246 · Cited by 62 · see citing works
Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction
Davagdorj, K., Bae, J., Pham, V.-H., Theera‐Umpon, N., & Ryu, K. H. (2021) · IEEE Access · doi:10.1109/access.2021.3110336 · Cited by 61 · see citing works
Song, D., Yao, J., Jiang, Y., Shi, S., Cui, C., Wang, L., Wang, L., Wu, H., Tian, H., Ye, X., et al. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107527 · Cited by 60 · see citing works
Talaat, F. M., Elnaggar, A. R., Shaban, W. M., Shehata, M., & Elhosseini, M. A. (2024) · Bioengineering · doi:10.3390/bioengineering11080822 · Cited by 58 · see citing works
Informing antimicrobial stewardship with explainable AI
Cavallaro, M., Moran, E., Collyer, B. S., McCarthy, N. D., Green, C., & Keeling, M. J. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000162 · Cited by 55 · see citing works
Severn, C., Suresh, K., Görg, C., Choi, Y. S., Jain, R., & Ghosh, D. (2022) · Sensors · doi:10.3390/s22145205 · Cited by 52 · see citing works
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
Wickstrøm, K., Mikalsen, K. Ø., Kampffmeyer, M., Revhaug, A., & Jenssen, R. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3042637 · Cited by 51 · see citing works
Debjit, K., Islam, M. S., Rahman, M. A., Pinki, F. T., Nath, R. D., Al-Ahmadi, S. A., Hossain, M. S., Mumenin, K. M., & Awal, M. A. (2022) · Diagnostics · doi:10.3390/diagnostics12051023 · Cited by 48 · see citing works
Martínez-Agüero, S., Soguero-Ruíz, C., Alonso, J. M., Mora-Jiménez, I., Álvarez-Rodríguez, J., & Marques, A. G. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.02.021 · Cited by 47 · see citing works
Laatifi, M., Douzi, S., Ezzine, H., Asry, C. E., Naya, A. W. B., Bouklouze, A., Zaid, Y., & Naciri, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-31542-7 · Cited by 47 · see citing works
Lalithadevi, B., & Krishnaveni, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18863-z · Cited by 46 · see citing works
Alkhalaf, S., Alturise, F., Bahaddad, A. A. S., Elnaim, B. M. E., Shabana, S., Abdel‐Khalek, S., & Mansour, R. F. (2023) · Cancers · doi:10.3390/cancers15051492 · Cited by 45 · see citing works
Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients
Juraev, F., El–Sappagh, S., Abdukhamidov, E., Ali, F., & Abuhmed, T. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104216 · Cited by 44 · see citing works
Explainable quantum clustering method to model medical data
Deshmukh, S., Behera, B. K., Mulay, P., Ahmed, E. A., Al-Kuwari, S. M., Tiwari, P., & Farouk, A. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110413 · Cited by 44 · see citing works
Interpretable Classification of Pneumonia Infection Using eXplainable AI (XAI-ICP)
Sheu, R., Pardeshi, M. S., Pai, K.-C., Chen, L.-C., Wu, C., & Chen, W.-C. (2023) · IEEE Access · doi:10.1109/access.2023.3255403 · Cited by 44 · see citing works
Chadaga, K., Prabhu, S., Vivekananda Bhat, K., Sampathila, N., Umakanth, S., & Chadaga, R. (2023) · Bioengineering · doi:10.3390/bioengineering10040439 · Cited by 44 · see citing works
Leveraging explainable artificial intelligence to optimize clinical decision support
Liu, S., McCoy, A. B., Peterson, J. F., Lasko, T. A., Sittig, D. F., Nelson, S. D., Andrews, J. C., Patterson, L., Cobb, C., Mulherin, D., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae019 · Cited by 44 · see citing works
Kim, K. H., Koo, H., Lee, B., Yoon, S., & Sohn, M. (2021) · Journal of the Korean Physical Society · doi:10.1007/s40042-021-00202-2 · Cited by 41 · see citing works
Time-series visual explainability for Alzheimer’s disease progression detection for smart healthcare
Rahim, N., Abuhmed, T., Mirjalili, S., El–Sappagh, S., & Muhammad, K. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.09.050 · Cited by 41 · see citing works
Islam, M. S., Awal, M. A., Laboni, J. N., Pinki, F. T., Karmokar, S., Mumenin, K. M., Al-Ahmadi, S. A., Rahman, M. A., Hossain, M. S., & Mirjalili, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105671 · Cited by 40 · see citing works
Chun, 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 · see citing works
Alfeo, A. L., Zippo, A. G., Catrambone, V., Cimino, M. G. C. A., Toschi, N., & Valenza, G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107550 · Cited by 38 · see citing works
Evans, R. P., Bryant, L. D., Russell, G., & Absolom, K. L. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105342 · Cited by 38 · see citing works
An explainable Artificial Intelligence software system for predicting diabetes
Srinivasu, P. N., Ahmed, S., Hassaballah, M., & Almusallam, N. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36112 · Cited by 38 · see citing works
Wireless Capsule Endoscopy Image Classification: An Explainable AI Approach
Varam, D., Mitra, R., Mkadmi, M., Riyas, R. A., Abuhani, D. A., Dhou, S., & Alzaatreh, A. (2023) · IEEE Access · doi:10.1109/access.2023.3319068 · Cited by 37 · see citing works
Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease
Ducange, P., Marcelloni, F., Renda, A., & Ruffini, F. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10332-x · Cited by 36 · see citing works
A computational framework of routine test data for the cost-effective chronic disease prediction
Liu, M., Zhou, J., Xi, Q., Liang, Y., Li, H., Liang, P., Guo, Y., Liu, M., Temuqile, T., Yang, L., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad054 · Cited by 32 · see citing works
Cervical cancer risk prediction with robust ensemble and explainable black boxes method
Curia, F. (2021) · Health and Technology · doi:10.1007/s12553-021-00554-6 · Cited by 31 · see citing works
Zhu, T., Liu, X., Wang, J., Kou, R., Hu, Y., Yuan, M., Yuan, C., Luo, L., & Zhang, W. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107723 · Cited by 31 · see citing works
Córdova, C., Muñoz, R., Olivares, R., Minonzio, J.-G., Lozano, C., González, P., Marchant, I., González‐Arriagada, W. A., & Olivero, P. (2022) · Oncology Letters · doi:10.3892/ol.2022.13630 · Cited by 30 · see citing works
Lalithadevi, B., Krishnaveni, S., & Gnanadurai, J. S. C. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01976-7 · Cited by 30 · see citing works
Development of an explainable artificial intelligence model for Asian vascular wound images
Lo, Z. J., Mak, M. H. W., Liang, S., Chan, Y. M., Goh, C. C., Lai, T. P., Tan, A. H. M., Thng, P., Rodriguez, J., Weyde, T., et al. (2023) · International Wound Journal · doi:10.1111/iwj.14565 · Cited by 29 · see citing works
Cavallaro, M., Moiz, H., Keeling, M. J., & McCarthy, N. D. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009121 · Cited by 28 · see citing works
Chen, H., Damarjati, C., Putra, K. T., Chen, H.-M., Hsieh, C., Lin, H., Wu, M., & Chen, C. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2021.102855 · Cited by 28 · see citing works
Zeng, Z., Tang, X., Liu, Y., He, Z., & Gong, X. (2022) · BioData Mining · doi:10.1186/s13040-022-00309-7 · Cited by 28 · see citing works
EXplainable AI for Decision Support to Obesity Comorbidities Diagnosis
Aiosa, G. V., Palesi, M., & Sapuppo, F. (2023) · IEEE Access · doi:10.1109/access.2023.3320057 · Cited by 27 · see citing works
Explainable Artificial Intelligence (XAI) Approach to Heart Disease Prediction
Sethi, A., Dharmavaram, S., & Somasundaram, S. K. (2024) · doi:10.1109/aiiot58432.2024.10574635 · Cited by 26 · see citing works
Beyond regulatory approaches to ethics: making space for ethical preparedness in healthcare research
Lyle, K., Weller, S., Samuel, G., & Lucassen, A. (2022) · Journal of Medical Ethics · doi:10.1136/medethics-2021-108102 · Cited by 24 · see citing works
Survey of user preferences from a comparative trial of UpToDate and ClinicalKey
Kronenfeld, M. R., Bay, R. C., & Coombs, W. (2013) · Journal of the Medical Library Association JMLA · doi:10.3163/1536-5050.101.2.011 · Cited by 23 · see citing works
Gkantzios, A., Kokkotis, C., Tsiptsios, D., Moustakidis, S., Gkartzonika, E., Avramidis, T., Aggelousis, N., & Vadikolias, Κ. (2023) · Diagnostics · doi:10.3390/diagnostics13030532 · Cited by 23 · see citing works
Improving trust and confidence in medical skin lesion diagnosis through explainable deep learning
Metta, C., Beretta, A., Guidotti, R., Yin, Y., Gallinari, P., Rinzivillo, S., & Giannotti, F. (2023) · International Journal of Data Science and Analytics · doi:10.1007/s41060-023-00401-z · Cited by 23 · see citing works
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Ammenwerth, E., Hyppönen, H., de Keizer, N. F., Nykänen, P., Rigby, M., Scott, P., Talmon, J. L., Georgiou, A., & Magrabi, F. (2016) · Yearbook of Medical Informatics · doi:10.15265/iy-2016-013 · Cited by 22 · see citing works
Factors influencing pharmacists' intentions to use Pharmacy Information Systems
Alanazi, A., Rabiah, F. A., Gadi, H. A., Househ, M. S., & Aldosari, B. (2018) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2018.02.004 · Cited by 22 · see citing works
Woodbright, M. D., Morshed, A., Browne, M., Ray, B. R., & Moore, S. T. (2024) · IEEE Access · doi:10.1109/access.2024.3375877 · Cited by 22 · see citing works
Nguyen, H. V., & Byeon, H. (2023) · Mathematics · doi:10.3390/math11092030 · Cited by 19 · see citing works
Explainable AI applications in the Medical Domain: a systematic review
Prentzas, N., Kakas, A., & Pattichis, C. S. (2023) · arXiv · doi:10.48550/arxiv.2308.05411 · Cited by 19 · see citing works
Prediction of ECOG Performance Status of Lung Cancer Patients Using LIME-Based Machine Learning
Nguyen, H. V., & Byeon, H. (2023) · Mathematics · doi:10.3390/math11102354 · Cited by 16 · see citing works
Settouti, N., & Saidi, M. (2023) · Evolutionary Intelligence · doi:10.1007/s12065-023-00833-3 · Cited by 14 · see citing works
Features and explainable methods for cytokines analysis of Dry Eye Disease in HIV infected patients
Curia, F. (2021) · Healthcare Analytics · doi:10.1016/j.health.2021.100001 · Cited by 12 · see citing works
Cho, K. H., Kim, E. S., Kim, J. W., Yun, C., Jang, J., Kasani, P. H., & Jo, H. S. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1166743 · Cited by 10 · see citing works
When time is of the essence: ethical reconsideration of XAI in time-sensitive environments
Wabro, A., Herrmann, M., & Winkler, E. C. (2024) · Journal of Medical Ethics · doi:10.1136/jme-2024-110046 · Cited by 10 · see citing works
Towards an explainable clinical decision support system for large-for-gestational-age births
Du, Y., Rafferty, A. R., McAuliffe, F. M., Mehegan, J., & Mooney, C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281821 · Cited by 9 · see citing works
Digital Health and Informatics Innovations for Sustainable Health Care Systems
(2024) · Studies in health technology and informatics · doi:10.3233/shti316 · Cited by 9 · see citing works
Hassan, M. M., AlQahtani, S. A., Al-Rakhami, M. S., & Elhendi, A. Z. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.047940 · Cited by 7 · see citing works
Towards an Explainable AI-Based Tool to Predict Preterm Birth
Kyparissidis-Kokkinidis, I., Logaras, E., Rigas, E. S., Tsakiridis, I. G., Dagklis, T., Billis, A. S., & Bamidis, P. D. (2023) · Studies in health technology and informatics · doi:10.3233/shti230207 · Cited by 5 · see citing works
Jung, I.-C., Zerlik, M., Schuler, K., Sedlmayr, M., & Sedlmayr, B. (2024) · Studies in health technology and informatics · doi:10.3233/shti240478 · Cited by 4 · see citing works
Inclusive Counterfactual Generation: Leveraging LLMs in Identifying Online Hate
Qureshi, M. A., Younus, A., & Caton, S. J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-62362-2_3 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Feder, G., M. Eccles, R. Grol, C. Griffiths, and J. Grimshaw. 1999. "Using Clinical Guidelines." British Medical Journal 318, no. 7185: 728-730.
- IEEE Xplore Digital Library. 2024. "IEEE Xplore Digital Library." https:// ieeex plore. ieee. org.
Cited by
Sorted by citations
Osman, K. A. Y. A., Shah, A. F. M. S., Karabulut, M. A., Karahan, S. N., Osmanca, M. S., & Acır, N. (2026) · IEEE Access · doi:10.1109/access.2026.3663161 · Cited by 21 · see citing works
Interpretable Machine Learning: A Comprehensive Review of Foundations, Methods, and the Path Forward
Fridkin, S., & Bendersky, M. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70075 · Cited by 23 · see citing works
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Alum, E. U., Egwu, C. K., Manjula, V. S., Ekpang, P. O., Ekpang, J. E., Echegu, D. A., Alum, B. N., & Uti, D. E. (2026) · Technology in Cancer Research & Treatment · doi:10.1177/15330338261434649 · Cited by 12 · see citing works
Empowering photodynamic therapy with artificial intelligence: current trends and future directions
Paul, A., Xavierselvan, M., Aebisher, D., Kubrak, T. P., Bartusik‐Aebisher, D., & Mallidi, S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1771804 · Cited by 8 · see citing works
Explainable Artificial Intelligence for Workplace Mental Health Prediction
Mokheleli, T., Bokaba, T., & Mbunge, E. (2025) · Informatics · doi:10.3390/informatics12040130 · Cited by 8 · see citing works
Qureshi, M. A., Noor, A. A., Manzoor, A., Mazhar Qureshi, M. D., Younus, A., & Rashwan, W. (2026) · PLOS One · doi:10.1371/journal.pone.0351473 · Cited by 7 · see citing works
Self-citationCurrent and future use of artificial intelligence in valvular heart disease imaging
Sengupta, P. P., Poterucha, T. J., Pezel, T., Tsang, T. S. M., & Cosyns, B. (2025) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeaf348 · Cited by 3 · see citing works
Hu, X., Liu, Y., Cao, Y., Wei, C., Liu, K., & Yang, J.-H. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1723169 · Cited by 2 · see citing works
Bhardwaj, T., & Sumangali, K. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1871960 · Cited by 2 · see citing works
Lim, Y. X., Ming, L. C., Ng, N. C., Leow, S., & Ingle, R. G. (2026) · Discover Computing · doi:10.1007/s10791-026-10205-x · Cited by 2 · see citing works
Dhillon, S. K., Singh, I., & Kaur, P. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3311-3.ch013 · Cited by 1 · see citing works
Artificial Intelligence (AI) in Patient–Healthcare Relationships: Psychosocial Perspective
Saikia, M. J., & Borthakur, D. (2025) · IEEE Access · doi:10.1109/access.2025.3611818 · Cited by 1 · see citing works
Kaur, R., Singh, B., & Kumar, R. (2025) · International Symposium on Advanced Electrical and Communication Technologies (ISAECT) · doi:10.1109/isaect68904.2025.11318794 · Cited by 1 · see citing works
Nimbalkar, M., Chandre, P. R., Shendkar, B. D., Jagdale, S. K., Arbat, R., & Dhopte, S. M. (2025) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig68706.2025.11323744 · Cited by 2 · see citing works
Color-CAM: Color-Coded Explainable AI for Dental Caries Segmentation
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Demystifying Explainable Artificial Intelligence (XAI) in Healthcare
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Skrodelis, H. K., & Romānovs, A. (2026) · WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL · doi:10.37394/23203.2026.21.5 · Cited by 1 · see citing works
babu, T., Nair, R. R., Kolla, T., Khoh, W. H., & Nair, J. K. M. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.429 · Cited by 1 · see citing works
Barki, C., Chouchen, M., Sediri, A., Ceylan, H. İ., Rahmouni, H. B., Muntean, R. I., El-Seedi, H. R., Bragazzi, N. L., & Dergaa, I. (2026) · Frontiers in Systems Biology · doi:10.3389/fsysb.2026.1892418 · Cited by 1 · see citing works
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Manurung, H. E., Budiman, F., Santoso, H. A., & Marjuni, A. (2025) · International Conference on Information System & Information Technology (ICISIT) · doi:10.1109/icisit66233.2025.11402942
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Tripathi, A., Singh, S., Padamandala, K., Khan, I. J., & Singh, S. (2026) · De Gruyter eBooks · doi:10.1515/9783112226223-006
Islam, F., Islam, A., Amin, M. A., & Zaber, M. I. (2026) · Journal of Medical Internet Research · doi:10.2196/87158
Muthukrishnan, P. J., & Packialatha, A. (2026) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt69054.2026.11502331
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Soladoye, A. A., Osborne, A., Ojo, A., Matope, E. M., Bamidele, D. B., & Olawade, D. B. (2026) · Advances in Biomarker Sciences and Technology · doi:10.1016/j.abst.2026.05.006
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Nguyen, P.-N., Nguyen, T.-H., Thang, N. T., & Nguyen, T.-L. (2026) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967261449358
Ehsani, A., Pasha, A. E., Mahmoudi, A., Rezaei, B., Ahari, S. G., Varjovi, M. N., Lashgari, S., Hosseini, S. P., khosravi, F., Sagharichi, M., et al. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100402
Iglesias, V. S., Modi, N. J., Purackal, R. S., Shoukat, K., Koyun, A., Nangolo, M. N., & Park, K. (2026) · Westcliff International Journal of Applied Research · doi:10.47670/wuwijar20262vnrkamk
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Al-Momany, A., Almomani, O., & Almomani, E. Y. (2026) · Diagnostics · doi:10.3390/diagnostics16132000
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Nguyen, L. N., & Wong, K.-C. (2026) · Research Square · doi:10.21203/rs.3.rs-10117658/v1
Tran, K. D., Zeng, X., Nguyen, D. H., Banfalvi, T., Miguélez, I. P., Labanieh, A. R., Soulat, D., & Tran, K. P. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2497-5_29
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Basha, S. J., Veeraiah, D., & Lingamgunta, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9119-9.ch003
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Self-citationHusaini, M. A., Putra, V. H. C., Tho, C., Lukmana, H. H., Raharja, A. R., & Asyaky, M. S. (2026) · International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS) · doi:10.1109/icadeis71120.2026.11644524
Moral distress risk profiling in ICU nurses: A cross-sectional study
Hu, W., & Li, X. (2026) · Nursing Ethics · doi:10.1177/09697330261474090
Dattu, V. S. N. C. H., Sampson, J., Sateesh, N., Gowthaman, B., Pai, V., & Naveenkumar, R. (2026) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece68961.2026.11645635
Russu, E.-A., Ilinca, R., Popa, L. G., Dumitriu, A. S., Păunica, S., Giurcăneanu, C., & Albu, C.-C. (2026) · Pharmaceuticals · doi:10.3390/ph19081292
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Hasnain, M., Hameed, S., Azim, K., & Waseem, M. (2026) · Exploration of Digital Health Technologies · doi:10.37349/edht.2026.1011102
Rettori, L., Rovini, E., Fujita, H., & Cavallo, F. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70131
Anil, V., Wilson, J., & Krishna, S. (2026) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct68978.2026.11700703
Amin, M. (2026) · Technology and Innovation Conference (TIC) · doi:10.1109/tic68483.2026.11702944
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Yavuz, B. Ç. (2026) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1917251
Brobbey, A. A., Bhosale¹, N., & Ackom, J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1864565
Jadhav, J. S., Margala, M., Kulkarni, S., & Chakrabarti, P. D. P. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.113588
Oloko-Oba, M., Esenogho, E., Aruleba, K., & Cameron, M. (2026) · AI · doi:10.3390/ai7110439
- Authors
- Noor, A. A., Manzoor, A., Mazhar Qureshi, M. D., Qureshi, M. A., & Rashwan, W.
- Affiliations
- Technological University Dublin (IE)
- eXplainable Analytics Group, Faculty of Bussiness Technological University of Dublin Dublin Ireland
- Corresponding author
- Qureshi, M. Atif
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2025-05-11
- Volume
- 15
- Issue
- 2
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W4410280591
- Cited by
- 52
- References
- 112
- Citations by year
- 2026: 43 · 2025: 9
- FWCI
- 20.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (publishedVersion, cc-by)
- Arrow - TU Dublin (Technological University Dublin) (submittedVersion, cc-by-nc-sa)
- Funders
- ADAPT - Centre for Digital Content Technology (13/RC/2106_P2)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- explainable AI, artificial intelligence in healthcare, XAI evaluation metrics, clinical decision support, diagnostic imaging, clinical text, feature selection, data preprocessing, neurology, cancer, AI regulation, AI transparency
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