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

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

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

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

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

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

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 147

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

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

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 22

Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving LabsPreprint

Sanders, L. M., Yang, J. H., Scott, R. T., Qutub, A., Martín, H. G., Berrios, D., Hastings, J. J. A., Rask, J., Mackintosh, G., Hoarfrost, A. L., et al. (2021) · arXiv.org · doi:10.48550/arxiv.2112.12582 · Cited by 3

Artificial Intelligence Approaches and Mechanisms for Big Data Analytics: A Systematic Study

Meena, S. G., & Dharani, S. (2023) · REST Journal on Data Analytics and Artificial Intelligence · doi:10.46632/jdaai/2/3/1 · Cited by 3

Advances in Health With the Help of Explainable AI

Shah, I. A., Murugesan, R. K., & Ashraf, H. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-2333-5.ch004 · Cited by 3

Risk score models for urinary tract infection hospitalization

Alizadeh, N., Vahdat, K., Shashaani, S., Swann, J., & Özaltın, O. Y. (2024) · PLoS ONE · doi:10.1371/journal.pone.0290215 · Cited by 3

A New XAI-based Evaluation of Generative Adversarial Networks for IMU Data Augmentation

Narteni, S., Orani, V., Ferrari, E., Verda, D., Cambiaso, E., & Mongelli, M. (2022) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom54947.2022.9982780 · Cited by 3

How to Distinguish Feigned from Genuine Depressive Symptoms: Response Patterns and Content Analysis of the SIMS Affective Disorder Scale

Ricci, E., Colasanti, M., Monaro, M., Mazza, C., Cardinale, A., Bosco, F., Mazzoni, G. A. L., Rossi‐Arnaud, C., Ferracuti, S., Domenico, A. D., et al. (2023) · Psychological Injury and Law · doi:10.1007/s12207-023-09481-5 · Cited by 2

Interpretable Predictive Models for Healthcare via Rational Multi-Layer Perceptrons

Suttaket, T., & Kok, S. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3671150 · Cited by 2

VisCARS: Knowledge Graph-Based Context-Aware Recommender System for Time-Series Data Visualization and Monitoring Dashboards

Moens, P., Volckaert, B., & Hoecke, S. V. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3414191 · Cited by 2

HUMANE: Harmonious Understanding of Machine Learning Analytics Network—global consensus for research on artificial intelligence in medicine

Deo, N., Nawaz, F. A., du Toit, C., Tran, T., Mamillapalli, C. K., Mathur, P., Reddy, S., Visweswaran, S., Prabhu, T. N., Moidu, K., et al. (2024) · Exploration of Digital Health Technologies · doi:10.37349/edht.2024.00018 · Cited by 2

User Satisfaction and Trust of AI Explanations in Threat Intelligence Analysis: Implications for Acceptance and Usability

(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets60972 · Cited by 3

Neurosurgery, Explainable AI, and Legal Liability

Matulionytė, R., Molina, E. S., & Ieva, A. D. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-64892-2_34 · Cited by 2

Healthcare CHATBOT for Diabetic Patients Using Classification

Naim, I., Singh, A. R., Sen, A., Sharma, A., & Mishra, D. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0707-4_39 · Cited by 2

Counterfactual Explanations for Rankings

Salimiparsa, M. (2023) · doi:10.21428/594757db.15b61c8c · Cited by 2

Human-Centric Enterprise Security: Advancing Access Control through AI-Driven AdministrationPreprint

Mariam, A., & Berrada, A. (2024) · doi:10.22541/au.170708972.23906177/v1 · Cited by 2

SHAP-Based Explainable Framework for Disease Prediction and Comorbidity Risk Assessment in HealthcarePreprint

Deshmukh, S. N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5368585 · Cited by 2

Machine Learning—Basic Unsupervised Methods (Cluster Analysis Methods, t-SNE)

Espadoto, M., Martins, S. B., Branderhorst, W., & Telea, A. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-36678-9_9 · Cited by 1

Memory optimized random forest classifier for EEG seizure detection in implantable monitoring and closed-loop neurostimulation devices

Kalousios, S., Larochelle, J., Zabler, N., Locker, N., Woias, P., Comella, L. M., Schulze‐Bonhage, A., & Dümpelmann, M. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/add76f · Cited by 1

Associations between daily physical activity timing and sleep efficiency revealed by explainable machine learning

Choe, J., Oh, Y., Park, C., Kim, Y., Wang, C., & Min, S. D. (2025) · Scientific Reports · doi:10.1038/s41598-025-32036-4 · Cited by 1

Leveraging Handwriting Dynamics, Explainable AI and Machine Learning for Alzheimer Prediction

Singh, S. K., & Chaturvedi, A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-81342-9_27 · Cited by 1

Real World Data and Artificial Intelligence in Diabetology

Kim, K. J. (2020) · Journal of Korean Diabetes · doi:10.4093/jkd.2020.21.3.140 · Cited by 1

Human-Centric Enterprise Security: Advancing Access Control through AI-Driven AdministrationPreprint

Mariam, A., Berrada, A., & Nakamura, S. (2024) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/6utz2 · Cited by 2

Real‐World Applications of Explainable AI in Healthcare

Urvi, Sharma, P., Goyal, K., & Sharma, S. (2025) · Wiley eBooks · doi:10.1002/9781394249312.ch20 · Cited by 2

Enlightened XAI

Hemalatha, P., Manikandan, J., Balaji, B., & Sujitha, V. (2025) · Wiley eBooks · doi:10.1002/9781394249312.ch25 · Cited by 1

IMMS: an intelligent medical monitoring system

Song, Z., Wang, Y., Fan, Z., & Zhang, Y. (2022) · International Conference on Biomedical and Intelligent Systems (IC-BIS) · doi:10.1117/12.2660559

Temporal Cohort Identification for Alzheimer’s Disease with Sequences of Clinical RecordsPreprint

Esitir, H., Azhir, A., Blacker, D., Ritchie, C. S., Patel, C. J., & Murphy, S. N. (2023) · medRxiv · doi:10.1101/2023.03.03.23286774

Einsatz neuer Technologien: Künstliche Intelligenz in der medizinischen Entscheidungsfindung – Status quo und Perspektiven einer interdisziplinären Herausforderung

Duwe, G., Mercier, D., Wiesmann, C., Junker, M., Haferkamp, A., Dengel, A., & Höfner, T. (2024) · Springer eBooks · doi:10.1007/978-3-658-43860-9_36

Smart Education Using Explainable Artificial Intelligence

Sharma, N. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-8151-9.ch004

Exploring Emerging Trends in AI for Global Healthcare Transformation

Seethalakshmi, S., Mohan, A., Marimuthu, U., & Alakumarimuthu, K. S. (2025) · Synthesis lectures on computer science · doi:10.1007/978-3-031-93673-9_3

Distinct neural correlates of active listening and passive listening to emotional narratives

Hung, R.-J., Wang, P.-Y., Low, I., Chen, Y., & Chen, L. (2025) · Cognitive Systems Research · doi:10.1016/j.cogsys.2025.101394 · Cited by 1

Transformer Models Enhance Explainable Risk Categorization of Incidents Compared to TF-IDF Baselines

Hölzing, C. R., Meybohm, P., Happel, O., Kranke, P., & Meynhardt, C. (2025) · AI · doi:10.3390/ai6090223

Explainable AI Principles of Building Industry 4.0

Sanjana, N., Immanual, R., Kirthika, K. M., & Sangeetha, S. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-94637-0_2

A novel explainable AI approach for reconstructing crosscut and hand-torn documents using machine learning

Desai, M. R., & Kannur, A. K. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04807-z

Patch-Based Graph Neural Network for Autism Classification via Eye Gaze Analysis

Verdone, A., Colonnese, F., Rosato, A., & Panella, M. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228803

Multimorbidity measurement strategies for predicting hospital visits

Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z

DT-sampler: A SAT-based Decision Tree Ensemble

Xue, X., Huang, C., Tsuda, K., & Das, D. (2025) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3787470.3787484

Heart Disease Prediction Using Machine Learning: A Comparative Analysis of Single and Ensemble Models in Healthcare

Sharifuddin, F. A. B. M., Fakharudin, A. S., Ali, A.-F. M., & Apandi, Z. F. M. (2025) · International Conference on Software Engineering and Computer Systems · doi:10.1109/icsecs65227.2025.11278993

SGAP-Net: Saliency-Guided Attention with Vessel-Aligned Interpretability for Retinal Disease Diagnosis

Samsumar, L. D., Imran, B., Efendi, M. M., Wahyudi, E., Basri, H., Junianto, M. B. S., Supardiantob, S., & Yani, A. (2025) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis68501.2025.11326976

Large Language Models na Identificação de Ideação Suicida em Textos não Clínicos em Inglês

Coutinho, L. C., Veras, A. E. R., Damasceno, R. C. P., & de Oliveira, A. C. (2026) · Revista Eletrônica de Iniciação Científica em Computação · doi:10.5753/reic.2026.7642

Transfer Learning in Thyroid Malignancy Prediction Explained with Grad-CAM

Sankar, S., & Sathyalakshmi, S. (2026) · Springer proceedings in mathematics & statistics · doi:10.1007/978-981-95-5891-9_22

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

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

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 20