Explainable AI for Healthcare: From Black Box to Interpretable Models

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294 · see citing works

Understanding deep image representations by inverting them

Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827 · see citing works

Distribution-Free Predictive Inference for Regression

Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1307116 · Cited by 1,049 · see citing works

Artificial intelligence in drug development: Present status and future prospects

Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works

Patient portals and health apps: Pitfalls, promises, and what one might learn from the other

Baldwin, J. L., Singh, H., Sittig, D. F., & Giardina, T. D. (2016) · Healthcare · doi:10.1016/j.hjdsi.2016.08.004 · Cited by 179 · see citing works

Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease

Liu, X., Chen, K., Wu, T., Weidman, D. A., Lure, F. Y. M., & Li, J. (2018) · Translational research · doi:10.1016/j.trsl.2018.01.001 · Cited by 141 · see citing works

Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow

Zheng, Q., Delingette, H., & Ayache, N. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.06.001 · Cited by 104 · see citing works

Learning From Explanations Using Sentiment and Advice in RL

Krening, S., Harrison, B. E., Feigh, K. M., Isbell, C. L., Riedl, M. O., & Thomaz, A. L. (2016) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2016.2628365 · Cited by 83 · see citing works

Mimir

Hicks, S. A., Eskeland, S. L., Lux, M., de Lange, T., Randel, K. R., Jeppsson, M., Pogorelov, K., Halvorsen, P., & Riegler, M. A. (2018) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3204949.3208129 · Cited by 21 · see citing works

Building interpretable models for polypharmacy prediction in older chronic patients based on drug prescription records

Kocbek, S., Kocbek, P., Stožer, A., Zupanič, T., Groza, T., & Štiglic, G. (2018) · PeerJ · doi:10.7717/peerj.5765 · Cited by 14 · see citing works

Expert identification of visual primitives used by CNNs during mammogram classification

Hsieh, S. S., Lehman, C. D., Dialani, V., Zhou, B., Peck, D., Patterson, G., Mackey, L. W., Syrgkanis, V., & Wu, J. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2293890 · Cited by 13 · see citing works

Scheduling Patients in Hospitals Based on Multi-agent Systems

Hsieh, F.-S., & Lin, J.-B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07455-9_4 · Cited by 11 · see citing works

Epidemic Outbreak Prediction Using Artificial Intelligence

Adhikari, N. C. D., Alka, A., Kurva, V. K., P, S. S., Nayak, H., Rishav, K., Nayak, A. K., Nayak, S. K., Shaj, V., & Karthikeyan (2018) · International Journal of Computer Science and Information Technology · doi:10.5121/ijcsit.2018.10405 · Cited by 10 · see citing works

Explanation in Expert Systemss: A Survey

Moore, J. D., & Swartout, W. (1988) · Cited by 6 · see citing works

Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flowPreprint

Zheng, Q., Delingette, H., & Ayache, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.03433 · Cited by 3 · see citing works

Heuristic Optimization Technique to Locate and Avoid Buried Landmines: Drone-based Approach

Alshbatat, A. I. N. (2018) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2018.11.05 · Cited by 2 · see citing works

Explainable, trustworthy, and ethical machine learning for healthcare: A survey

Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106043 · Cited by 372 · see citing works

Applications of Explainable Artificial Intelligence in Diagnosis and Surgery

Zhang, Y., Weng, Y., & Lund, J. (2022) · Diagnostics · doi:10.3390/diagnostics12020237 · Cited by 368 · see citing works

A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques

Nazar, M., Alam, M. M., Yafi, E., & Su'ud, M. M. (2021) · IEEE Access · doi:10.1109/access.2021.3127881 · Cited by 312 · see citing works

Artificial intelligence in cancer research, diagnosis and therapy

Elemento, O., Leslie, C., Lundin, J., & Tourassi, G. (2021) · Nature Reviews Cancer · doi:10.1038/s41568-021-00399-1 · Cited by 264 · see citing works

From promise to practice: towards the realisation of AI-informed mental health care

Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251 · see citing works

Artificial Intelligence in Healthcare: Review, Ethics, Trust Challenges & Future Research Directions

Kumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105894 · Cited by 236 · see citing works

Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review

(2023) · doi:10.1109/access.2023.3285596 · Cited by 205 · see citing works

Human-Centered Design to Address Biases in Artificial Intelligence

Chen, Y., Clayton, E. W., Novak, L. L., Anders, S. H., & Malin, B. (2023) · Journal of Medical Internet Research · doi:10.2196/43251 · Cited by 211 · see citing works

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Wani, N. A., Kumar, R., Mamta, ­., Bedi, J., & Rida, I. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102472 · Cited by 171 · see citing works

Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond

Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152 · see citing works

Explainable Artificial Intelligence for Tabular Data: A Survey

Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135 · see citing works

A survey on medical explainable AI (XAI): recent progress, explainability approach, human interaction and scoring system

Sheu, R.-K., & Pardeshi, M. S. (2022) · Sensors · doi:10.3390/s22208068 · Cited by 129 · see citing works

Explainable artificial intelligence for drug discovery and development: a comprehensive survey

Alizadehsani, R., Oyelere, S. S., Hussain, S., Jagatheesaperumal, S. K., Calixto, R. R., Rahouti, M., Roshanzamir, M., & De Albuquerque, V. H. C. (2024) · IEEE Access · doi:10.1109/access.2024.3373195 · Cited by 124 · see citing works

Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based

McCoy, L. G., Brenna, C. T. A., Chen, S. S., Vold, K., & Das, S. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.11.001 · Cited by 118 · see citing works

The role of AI for developing digital twins in healthcare: The case of cancer care

Kaul, R., Ossai, C. I., Forkan, A. R. M., Jayaraman, P. P., Zelcer, J., Vaughan, S., & Wickramasinghe, N. S. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1480 · Cited by 125 · see citing works

Local interpretable model-agnostic explanation approach for medical imaging analysis: A systematic literature review

Hassan, S. U., Abdulkadir, S. J., Zahid, M. S. M., & Al-Selwi, S. M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109569 · Cited by 121 · see citing works

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

Chaddad, A., Lu, Q., Li, J., Katib, Y., Kateb, R., Tanougast, C., Bouridane, A., & Abdulkadir, A. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123123 · Cited by 112 · see citing works

Interpreting deep learning models for epileptic seizure detection on EEG signals

Gabeff, V., Teijeiro, T., Zapater, M., Cammoun, L., Rheims, S., Ryvlin, P., & Atienza, D. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102084 · Cited by 101 · see citing works

Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review

Jung, J., Lee, H., Jung, H., & Kim, H. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16110 · Cited by 87 · see citing works

A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions

Saghiri, A. M., Vahidipour, S. M., Jabbarpour, M. R., Sookhak, M., & Forestiero, A. (2022) · Applied Sciences · doi:10.3390/app12084054 · Cited by 101 · see citing works

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

Singh, M., Pujar, G. V., Kumar, S. A., Bhagyalalitha, M., Akshatha, H. S., Abuhaija, B., Alsoud, A. R., Abualigah, L., Beeraka, N. M., & Gandomi, A. H. (2022) · Electronics · doi:10.3390/electronics11172634 · Cited by 93 · see citing works

A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer’s Disease

Lombardi, A., Diacono, D., Amoroso, N., Biecek, P., Monaco, A., Bellantuono, L., Pantaleo, E., Logroscino, G., Blasi, R. D., Tangaro, S., et al. (2022) · Brain Informatics · doi:10.1186/s40708-022-00165-5 · Cited by 76 · see citing works

Assessment of Performance, Interpretability, and Explainability in Artificial Intelligence–Based Health Technologies: What Healthcare Stakeholders Need to Know

Farah, L., Murris, J., Borget, I., Guilloux, A., Martelli, N., & Katsahian, S. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.02.004 · Cited by 75 · see citing works

Metaverse assisted Telesurgery in Healthcare 5.0: An interplay of Blockchain and Explainable AI

Bhattacharya, P., Obaidat, M. S., Savaliya, D., Sanghavi, S., Tanwar, S., & Sadaun, B. (2022) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits55221.2022.9832978 · Cited by 72 · see citing works

Tackling class imbalance in computer vision: a contemporary review

Saini, M., & Susan, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10557-6 · Cited by 68 · see citing works

An Empirical Evaluation of AI Deep Explainable Tools

Hailemariam, Y., Yazdinejad, A., Parizi, R. M., Srivastava, G., & Dehghantanha, A. (2020) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps50303.2020.9367541 · Cited by 63 · see citing works

Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey

Ploug, T., Sundby, A., Moeslund, T. B., & Holm, S. (2021) · Journal of Medical Internet Research · doi:10.2196/26611 · Cited by 63 · see citing works

Mini-review: Recent advances in post-translational modification site prediction based on deep learning

Meng, L., Chan, W.-S., Huang, L., Liu, L., Chen, X., Zhang, W., Wang, F., Cheng, K., Sun, H., & Wong, K. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.06.045 · Cited by 53 · see citing works

Biological research and self-driving labs in deep space supported by artificial intelligence

Sanders, L., Scott, R. T., Yang, J. H., Qutub, A. A., Martín, H. G., Berrios, D. C., Hastings, J. J. A., Rask, J. C., Mackintosh, G., Hoarfrost, A., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00618-4 · Cited by 55 · see citing works

ForecastExplainer: Explainable household energy demand forecasting by approximating shapley values using DeepLIFT

Shajalal, M., Boden, A., & Stevens, G. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123588 · Cited by 44 · see citing works

Artificial intelligence in Immuno-genetics

Farzan, R. A. (2024) · Bioinformation · doi:10.6026/973206300200029 · Cited by 38 · see citing works

Mental Health Diagnosis: A Case for Explainable Artificial Intelligence

Antoniou, G., Papadakis, E. P., & Baryannis, G. (2022) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213022410032 · Cited by 38 · see citing works

Explaining smartphone-based acoustic data in bipolar disorder: Semi-supervised fuzzy clustering and relative linguistic summaries

Kaczmarek‐Majer, K., Casalino, G., Castellano, G., Hryniewicz, O., & Dominiak, M. (2021) · Information Sciences · doi:10.1016/j.ins.2021.12.049 · Cited by 36 · see citing works

Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology

Duwe, G., Mercier, D., Wiesmann, C., Kauth, V., Moench, K., Junker, M., Neumann, C., Haferkamp, A., Dengel, A., & Höfner, T. (2024) · Cancer Medicine · doi:10.1002/cam4.7398 · Cited by 31 · see citing works

An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device

Pini, N., Ong, J. L., Yılmaz, G., Chee, N. I. Y. N., Siting, Z., Awasthi, A., Biju, S., Kishan, K., Patanaik, A., Fifer, W. P., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.974192 · Cited by 27 · see citing works

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Ursin, F., Lindner, F., Ropinski, T., Salloch, S., & Timmermann, C. (2023) · Ethik in der Medizin · doi:10.1007/s00481-023-00761-x · Cited by 28 · see citing works

An Explainable Regression Framework for Predicting Remaining Useful Life of Machines

Khan, T., Ahmad, K., Khan, J., Khan, I., & Ahmad, N. (2022) · International Conference on Automation and Computing (ICAC) · doi:10.1109/icac55051.2022.9911162 · Cited by 26 · see citing works

Explainable AI in Orthopedics: Challenges, Opportunities, and Prospects

Amirian, S., Carlson, L., Gong, M. F., Lohse, I., Weiss, K. R., Plate, J. F., & Tafti, A. P. (2023) · Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE) · doi:10.1109/csce60160.2023.00230 · Cited by 25 · see citing works

Machine Learning in Healthcare: Current Trends and the Future

Usmani, U. A., & Jaafar, J. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-2183-3_64 · Cited by 25 · see citing works

The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

Selvi, G. C., Murugan, R., Yenduri, G., Gurrammagari, D. R., Bhulakshmi, D., Kandati, D. R., Supriya, Y., Gadekallu, T. R., Rathore, R. S., & Jhaveri, R. H. (2023) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558266152231128060222 · Cited by 22 · see citing works

Effect of Explainable Artificial Intelligence on Trust of Mental Health Professionals in an AI-Based System for Suicide Prevention

de Oliveira, A. C., Azevedo, J. P. C., Ruback, L., Moreira, R., Teixeira, S. S., & Teles, A. S. (2025) · IEEE Access · doi:10.1109/access.2025.3556245 · Cited by 24 · see citing works

Investigation of a Web-Based Explainable AI Screening for Prolonged Grief Disorder

She, W. J., Ang, C. S., Neimeyer, R. A., Burke, L. A., Zhang, Y., Jatowt, A., Kawai, Y., Hu, J., Rauterberg, M., Prigerson, H. G., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3163311 · Cited by 25 · see citing works

Meaningful Explanation Effect on User’s Trust in an AI Medical System: Designing Explanations for Non-Expert Users

Larasati, R. A., Liddo, A. D., & Motta, E. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3631614 · Cited by 21 · see citing works

Explainable Impact of Partial Supervision in Semi-Supervised Fuzzy Clustering

Kmita, K., Kaczmarek‐Majer, K., & Hryniewicz, O. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3370768 · Cited by 21 · see citing works

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

Nguyen, D.-K., Chan, C., Li, A., Phan, D., & Lan, C.-H. (2022) · Health Informatics Journal · doi:10.1177/14604582221137537 · Cited by 20 · see citing works

EHR-based prediction modelling meets multimodal deep learning: A systematic review of structured and textual data fusion methods

Teles, A. S., de Moura, I. R., Silva, F., Roberts, A., & Stahl, D. R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102981 · Cited by 22 · see citing works

Test Input Prioritization for Machine Learning Classifiers

Dang, X., Li, Y., Papadakis, M., Klein, J., Bissyandé, T. F., & Traon, Y. L. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3350019 · Cited by 19 · see citing works

Clinical needs and preferences for AI-based explanations in clinical simulation training

Kollerup, N. K., Johansen, S. S., Tolsgaard, M. G., Friis, M. L., Skov, M. B., & van Berkel, N. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2334852 · Cited by 19 · see citing works

Artificial intelligence and its application for cardiovascular diseases in Chinese medicine

Chen, X., LEUNG, Y.-L. A., & Jiangang, S. H. E. N. (2022) · Digital Chinese Medicine · doi:10.1016/j.dcmed.2022.12.003 · Cited by 17 · see citing works

Explainable AI for Breast Cancer Detection: A LIME-Driven Approach

Khater, T., Hussain, A. J., Mahmoud, S. A., & Yasen, S. (2023) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese60595.2023.10469341 · Cited by 17 · see citing works

Multi-Modal Explainable Artificial Intelligence for neural network-based tool wear detection in machining

Sotubadi, S. V., Pallissery, S. S., & Nguyen, V. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110141 · Cited by 16 · see citing works

Computational complexity in explainable decision support system: A review

Ezeji, I. N., Adigun, M. O., & Oki, O. A. (2024) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-219407 · Cited by 14 · see citing works

Explainable Deep Contrastive Federated Learning System for Early Prediction of Clinical Status in Intensive Care Unit

Nguyen, T.-N., Yang, H.-J., Kho, B., Kang, S., & Kim, S.-H. (2024) · IEEE Access · doi:10.1109/access.2024.3447759 · Cited by 14 · see citing works

Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model

Arjaria, S. K., Rathore, A. S., & Chaubey, G. (2021) · International Journal of Business Analytics · doi:10.4018/ijban.292058 · Cited by 14 · see citing works

Temporal characterization of Alzheimer's Disease with sequences of clinical records

Estiri, H., Azhir, A., Blacker, D., Ritchie, C. S., Patel, C. J., & Murphy, S. N. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104629 · Cited by 13 · see citing works

Regulatory Challenges in AI/ML-Enabled Medical Devices: A Scoping Review and Conceptual Framework

Kaladharan, S., Manayath, D., & Rejikumar, G. (2024) · Journal of Medical Devices · doi:10.1115/1.4066280 · Cited by 12 · see citing works

Explainable Artificial Intelligence (XAI) in glaucoma assessment: Advancing the frontiers of machine learning algorithms

Nimmy, S. F., Hussain, O. K., Chakrabortty, R. K., & Saha, S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113333 · Cited by 15 · see citing works

Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology

Groh, R., Lei, Z., Martignetti, L., Li‐Jessen, N. Y. K., & Kist, A. M. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202100284 · Cited by 11 · see citing works

Explainable Artificial Intelligence in Health Care: How XAI Improves User Trust in High-Risk Decisions

Praveen, S., & Joshi, K. K. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-18292-1_6 · Cited by 10 · see citing works

Explainable AI in Healthcare

Upendran, S. V. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-5468-1.ch004 · Cited by 10 · see citing works

Application of deep learning in wound size measurement using fingernail as the reference

Chang, D., Nguyen, D.-K., Nguyen, T.-N., & Chan, C. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02778-8 · Cited by 11 · see citing works

A systematic literature review on transparency and interpretability of AI models in healthcare: taxonomies, tools, techniques, datasets, open research challenges, and future trends

Shafik, W., Hidayatullah, A. F., Kalinaki, K., Gul, H., Zakari, R. Y., & Tufail, A. (2026) · Health and Technology · doi:10.1007/s12553-025-01051-w · Cited by 18 · see citing works

How can machine learning identify suicidal ideation from user's texts? Towards the explanation of the Boamente system

de Oliveira, A. C., Diniz, E. J. D. S., Teixeira, S. S., & Teles, A. S. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.093 · Cited by 10 · see citing works

Actionable absolute risk prediction of atherosclerotic cardiovascular disease based on the UK Biobank

Kesar, A., Baluch, A., Barber, O., Hoffmann, H., Jovanovic, M., Renz, D., Stopak, B. L., Wicks, P. J., & Gilbert, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263940 · Cited by 10 · see citing works

Developing interpretable models and techniques for explainable AI in decision-making

Jagannathan, J., Agrawal Rajesh, K., Labhade-Kumar, N., Rastogi, R., Unni, M. V., & Baseer, K. K. (2023) · THE SCIENTIFIC TEMPER · doi:10.58414/scientifictemper.2023.14.4.39 · Cited by 12 · see citing works

Boundaries and Future Trends of ChatGPT Based on AI and Security Perspectives

Alsumayt, A., Alfawaer, Z. M., El-Haggar, N., Alshammari, M., Alghamedy, F. H., Aljameel, S. S., Alabbad, D. A., & Aldossary, M. I. (2024) · HighTech and Innovation Journal · doi:10.28991/hij-2024-05-01-010 · Cited by 10 · 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

Basic Issues and Challenges on Explainable Artificial Intelligence (XAI) in Healthcare Systems

Oladipo, I. D., Awotunde, J. B., AbdulRaheem, M., & Salihu, S. A. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-3791-9.ch011 · Cited by 9 · see citing works

Liver Disease Classification - An XAI Approach to Biomedical AI

Agbozo, E., & Balungu, D. M. (2024) · Informatica · doi:10.31449/inf.v48i1.4611 · Cited by 9 · see citing works

Frequent temporal patterns of physiological and biological biomarkers and their evolution in sepsis

Jazayeri, A. R., Yang, C. C., & Capan, M. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102576 · Cited by 8 · see citing works

Explainable evaluation of generative adversarial networks for wearables data augmentation

Narteni, S., Orani, V., Ferrari, E., Verda, D., Cambiaso, E., & Mongelli, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110133 · Cited by 8 · see citing works

Charting exposomethics: a roadmap for the ethical foundations of the human exposome project

Sillé, F. C. M., Belkadi, M., Koehler, K., Ali, J., Vasiliou, V., Sarigiannis, D. A., & Härtung, T. (2026) · Human Genomics · doi:10.1186/s40246-025-00906-7 · Cited by 8 · see citing works

A review of applications of artificial intelligence in cardiorespiratory rehabilitation

Raja, M. A., Loughran, R., & McCaffery, F. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101327 · Cited by 8 · see citing works

Human-Centered Explanations: Lessons Learned from Image Classification for Medical and Clinical Decision Making

Finzel, B. (2024) · Künstliche Intell. · doi:10.1007/s13218-024-00835-y · Cited by 8 · see citing works

A procedural overview of why, when and how to use machine learning for psychiatry

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Introduction to Explainable AI

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The multilayer community structure of medulloblastoma

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Interpretable Models For ML-Based Classification of Obesity

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A Review of Biosensors and Artificial Intelligence in Healthcare and their Clinical Significance

Tariq, M. (2024) · Psychology & Psychological Research International Journal · doi:10.23880/pprij-16000392 · Cited by 8 · see citing works

Toward collaborative artificial intelligence development for animal well-being

Sun, J. (2025) · Journal of the American Veterinary Medical Association · doi:10.2460/javma.24.10.0650 · Cited by 7 · see citing works

Towards Transparent Deep Learning in Medicine: Feature Contribution and Attention Mechanism-Based Explainability

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Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs)

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Unveiling Black-Boxes: Explainable Deep Learning Models for Patent Classification

Shajalal, M., Denef, S., Karim, M. R., Boden, A., & Stevens, G. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44067-0_24 · Cited by 6 · see citing works

HealthEdgeAI: GAI and XAI Based Healthcare System for Sustainable Edge AI and Cloud Computing Environments

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COVID-19 vaccine prediction based on an interpretable CNN-LSTM model with three-stage feature engineering

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Analysis of Cardiovascular Disease Prediction Using Model-Agnostic Explainable Artificial Intelligence Techniques

Kavila, S. D., Bandaru, R., Gali, T. V. M. B., & Shafi, J. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-3791-9.ch002 · Cited by 6 · see citing works

A Bibliographic Study of Macular Fovea Detection: AI-Based Methods, Applications, and Issues

Wang, H., Li, Z., Xing, L., Chong, K. K. L., Zhou, X., Wang, F., Zhou, J., & Li, Z. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-7184-6_24 · Cited by 6 · see citing works

A Systematic Literature Review on Transparencyand Interpretability of AI models in Healthcare:Taxonomies, Tools, Techniques, Datasets, OpenResearch Challenges, and Future TrendsPreprint

Shafik, W., Hidayatullah, A. F., Kalinaki, K., Gul, H., Zakari, R. Y., & Tufail, A. (2024) · Research Square · doi:10.21203/rs.3.rs-4419881/v1 · Cited by 6 · see citing works

Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations

Chen, Y., Zhong, R., Ri, N., Zhao, C., He, H., Steinhardt, J., Yu, Z., & McKeown, K. (2023) · arXiv · doi:10.48550/arxiv.2307.08678 · Cited by 5 · see citing works

Explainable AI in Health Care: Trust and Transparency in AI-Powered Medical Diagnosis

Mohammed, Z. K. (2025) · Artificial intelligence · doi:10.5772/intechopen.1011279 · Cited by 7 · see citing works

Evaluating How Explainable AI Is Perceived in the Medical Domain: A Human-Centered Quantitative Study of XAI in Chest X-Ray Diagnostics

Karagoz, G. N., van Kollenburg, G., Özçelebi, T., & Meratnia, N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-67751-9_8 · Cited by 5 · see citing works

An Explainable AI-Enabled Framework for the Diabetes Classification

Long, C. K., Puri, V., Solanki, V. K., & Aponte, G. J. R. (2023) · IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) · doi:10.1109/icmlant59547.2023.10372975 · Cited by 5 · see citing works

Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians’ use of machine learning in perioperative care

Gonzalez, X. T., Steger-May, K., & Abraham, J. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae257 · Cited by 4 · see citing works

Explainable event recognition

Khan, I., Ahmad, K., Gul, N., Khan, T., Ahmad, N., & Al‐Fuqaha, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14832-0 · Cited by 5 · see citing works

Epileptic Seizure Prediction Based on Synchroextracting Transform and Sparse Representation

Ra, J. S., Li, T., & Li, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3514859 · Cited by 4 · see citing works

AI Powered Identification of Drug Targets and Pathways for Diagnosis and Treatment Planning: A Review

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Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records

Hanis, T. M., Ruhaiyem, N. I. R., Arifin, W. N., Haron, J., Rahman, W. F. W. A., Abdullah, R., & Musa, K. I. (2022) · Diagnostics · doi:10.3390/diagnostics12112826 · Cited by 4 · see citing works

Towards Artificial Intelligence-enabled Medical Pre-operative Airway Assessment

Lin, Q., Chng, C.-B., Too, J. J. Y., Zhang, J., Liu, H., Foong, T. W., Loh, N. H. W., & Chui, C. (2022) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom54947.2022.9982781 · Cited by 4 · see citing works

Exploring Explainable Artificial Intelligence in Healthcare: Issues, Challenges, and Opportunities

Dubey, A., Bhanodia, P. K., Rathore, N. P. S., Sethi, K. K., & Khamparia, A. (2025) · Microorganisms for sustainability · doi:10.1007/978-981-96-3448-4_14

Explainable AI and Interpretability in Medical AI Models

(2024) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789811284113_0007

Explainable Artificial Intelligence (XAI) in Healthcare: Enhancing Transparency and Trust

Inukonda, J., Tetala, V. R. R., & Hallur, J. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i06.30010 · Cited by 15 · see citing works

Should I trust this model? Explainability and the black box of artificial intelligence in medicine

Petch, J., Bortesi, J. P. T., Nelson, W., Di, S., & Mamdani, M. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00015-6 · Cited by 7 · see citing works

A systematic literature review on transparency and interpretability of AI models in healthcare: taxonomies, tools, techniques, datasets, open research challenges, and future trends

Shafik, W., Hidayatullah, A. F., Kalinaki, K., Gul, H., Zakari, R. Y., & Tufail, A. (2026) · Health and Technology · doi:10.1007/s12553-025-01051-w · Cited by 18 · see citing works