Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine?

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148

Scalable and accurate deep learning with electronic health records

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · cited 1×

Clinically applicable deep learning for diagnosis and referral in retinal disease

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639

Scalable and accurate deep learning with electronic health records.

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242

Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community

van Walraven, C., Dhalla, I. A., Bell, C. M., Etchells, E. E., Stiell, I. G., Zarnke, K. B., Austin, P. C., & Forster, A. J. (2010) · Canadian Medical Association Journal · doi:10.1503/cmaj.091117 · Cited by 996

An interpretable machine learning model for accurate prediction of sepsis in the ICU

Nemati, S., Holder, A., Razmi, F., Stanley, M. D., Clifford, G. D., & Buchman, T. G. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000002936 · Cited by 832

Multiomics Research Strategies in Cancer: A Growing and Innovative Field

Du, Z., Liu, X., Lv, Z., Wang, B., Xia, Y., Al-duais, W. A. M., Yan, L., Zhang, F., & Li, Y. (2026) · MedComm · doi:10.1002/mco2.70644 · Cited by 1

Visualizing Coatnet Predictions for Aiding Melanoma Detection

Carebot s.r.o, P., & Kvak, D. (2022) · Engineering and Technology Journal · doi:10.47191/etj/v7i7.01 · Cited by 1

Main requirements of end-to-end deep learning models for biomedical time series classification in healthcare environments

Alarcón, Á. S., Madrid, N. M., Seepold, R., & Ramirez, J. A. O. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.532 · Cited by 1

MuTATE—an R package for comprehensive multi-objective molecular modeling

Ayton, S. G., & Treviño, V. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad507 · Cited by 1

Deep Learning—Prediction

Gerges, C. A., Vessies, M. B., van de Leur, R. R., & van Es, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-36678-9_12 · Cited by 1

Artificial Intelligence in Laboratory Technologies for Early Detection and Prognostication of Sepsis: A Systematic Review

Bakouri, M., Alqahtani, N. M., Alhussain, O. M., Alrashidi, N., Almutairi, S. N., Alabdulwahab, A. O., Alaskar, B. S., & Alqahtani, M. A. (2023) · Journal of Pioneering Medical Science · doi:10.61091/jpms202312310 · Cited by 2

Importance Sampling to Learn Vasopressor Dosage to Optimize Patient Mortality in an Interpretable Manner

Rastogi, A., & Mentor, D. S. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825148 · Cited by 1

Model-Based Q-Learning with Monotone Policies for Personalized Management of Hypertension

Marrero, W. J., & Yi, L. (2024) · Winter Simulation Conference · doi:10.1109/wsc63780.2024.10838907 · Cited by 1

Explaining alerts from a pediatric risk prediction model using clinical text

Nycklemoe, S., Devarapu, S., Gao, Y., Carey, K. A., Kuehnel, N., Munjal, N. K., Jani, P., Churpek, M. M., Dligach, D., Afshar, M., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf121 · Cited by 1

Why Attentions May Not Be Interpretable?Preprint

Bai, B., Liang, J., Zhang, G., Li, H., Bai, K., & Wang, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05656 · Cited by 4

Exploring the Explainability of a Machine Learning Model for Prostate Cancer: Do Lesions Localize with the Most Important Feature Maps?Preprint

Provenzano, D., Haji-Momenian, S., Batheja, V., & Loew, M. H. (2024) · medRxiv · doi:10.1101/2024.10.12.24315347 · Cited by 2

Expanding the Scope of Ai Readiness: Validation of the Mairs Scale Among Dental, Nursing, and Midwifery StudentsPreprint

Koç, A. D. (2025) · Research Square · doi:10.21203/rs.3.rs-6414139/v1 · Cited by 1

Deep Learning for Freezing of Gait Assessment using Inertial Measurement Units: A Multicentre StudyPreprint

Yang, P.-K., Carlon, J., Goris, M., Klaver, E., Nonnekes, J. H., Wezel, R. J. A. V., Alcock, L., Yarnall, A. J., Rochester, L., Hansen, C., et al. (2025) · medRxiv · doi:10.1101/2025.06.27.25330405 · Cited by 1

In theory-Sharing AI’s black box sounds great-But in reality it’s not

Fleming Richard, M., Fleming Matthew, R., & Chaudhuri Tapan, K. (2020) · Journal of Cardiovascular Medicine and Cardiology · doi:10.17352/2455-2976.000103

Predicting Type 1 Diabetes Onset using Novel Survival Analysis with Biomarker Ontology.

Li, Y., Liu, B., Anand, V., Dunne, J. L., Lundgren, M., Ng, K., Rewers, M., Veijola, R., & Ghalwash, M. F. (2020) · PubMed Central

DEVIANCE MINING OF ONLINE PROCESSES WITH NONATOMIC EVENTS IN THE COVID-19 DOMAIN

Jazbik, L. S. (2022) · doi:10.17771/pucrio.acad.60847

Desafios e Tendências na Predição de Sepse

Silva, L. G., Jr., Andrade, R. M. C., & Celestino, J. (2023) · doi:10.5753/sbcas.2023.230163

Future Applications of Handheld POCUS

Fryman, C. J., & Mayo, P. H. (2023) · Springer eBooks · doi:10.1007/978-3-031-29472-3_30

Robust Interpretability Approach to Enhance Trustworthy AI in Healthcare: A Systematic Review of the Last Decade Toward Proposed FrameworkPreprint

Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K. (2023) · Research Square · doi:10.21203/rs.3.rs-3626164/v1

Medicinal Time Reminder and Touchless Dispenser Robot in Hospitals for Elderly Patients and Patients Having Contagious Disease

Jinnah, A. B., Kar, J., Puthanveedu, P. K., al Abri, S. K. K. S., & al Abri, A. K. S. G. (2023) · International Journal of All Research Education & Scientific Methods · doi:10.56025/ijaresm.2023.1110231995

Risk management of antibacterial drugs: A decision tree classification method driven by real world data

Wei, J., & Ma, H.-Y. (2023) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei60920.2023.10373351

Unlocking the Potential of AI in Cancer Therapeutics: Advancements in Treatment Selection, Swift Diagnosis, Risk Assessment, and Prognosis

Yadav, D. K., Rathee, S., & Patil, U. K. (2024) · Current Cancer Therapy Reviews · doi:10.2174/0115733947301672240528061642 · Cited by 1

Prognostic Value of CD8+ T-Cells at the Invasive Margin is Comparable to Immune Score in Non-Metastatic Colorectal Cancer: Prospective Multicenter Cohort StudyPreprint

Wankhede, D., Halama, N., Kloor, M., Edelmann, D., Brenner, H., & Hoffmeister, M. (2024) · medRxiv · doi:10.1101/2024.09.23.24314210

MuTATE: an interpretable multi-endpoint machine learning framework for automated molecular subtyping in cancer

Ayton, S. G., Pavlicová, M., Robles‐Espinoza, C. D., Fuentes-Aguilar, R. Q., Garza-Hernandez, D., Martínez-Ledesma, E., Tamez‐Peña, J. G., Garcia-Pompermayer, M. R., & Treviño, V. (2025) · npj Health Systems · doi:10.1038/s44401-025-00025-4

Evaluating multiple large language models on orbital diseases

Yang, Q., Zeng, Y.-M., Wei, H., Chen, C., Ling, Q., Wang, X., Chen, X., & Shao, Y. (2025) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2025.1574378

Artificial intelligence in disease prediction and diagnosis: a systematic literature review

Harshith, R., Pranathi, K., Navya, B., Kavitha, T., Sai Keerthi, K., Devi Vara Prasad, R., & Hema Kumari, N. (2025) · International Journal of Current Innovations in Advanced Research · doi:10.47957/ijciar.v8i3.207

AI Act Demands for Responsible Innovation: Where to go From Here?

Jessen, R., Yeung, K. K., & Ploem, M. C. (2025) · Journal of Endovascular Therapy · doi:10.1177/15266028251388730

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

Differentially Private RL for ICU Decision Support: A Comparative Analysis

Batchala, R., Jain, P., Gyanchandani, M., & Shukla, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10940-8_32

Artificial Intelligence in Healthcare: Strategic Value, Constraints, and a Governance-First Integration Framework

Zheng, S., & Liu, M. (2025) · International Conference on Artificial Intelligence and Sustainable Development · doi:10.1145/3786484.3786512

Ethical considerations in the clinical application of prediction tools for severe mental disorders: perspectives from child and adolescent psychiatrists

Neiders, I., Kalēja, J., Mileiko, I., Poļaka, I., & Mežinska, S. (2026) · Global Bioethics · doi:10.1080/11287462.2026.2648374

Apprentissage profond pour l'aide au diagnostic et comparaison des mécanismes d'explicabilité avec l'attention visuelle humaine : application à la détection de la maladie de Crohn

Vallée, R. (2022) · theses.fr (ABES) · doi:10.70675/d01054a7z79b7z401dz90e8z8db2c54903f8

Evaluation of large language models for nursing support in maternal venous thromboembolism care

Li, C., Lin, Y., Chen, D.-P., & Huang, Y. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1740254 · Cited by 1

Cyberbullying as a form of digital aggression: psychosocial characteristics and mental health impact

Bazhanova, L. S., & Олегович, Б. П. (2026) · Psikhiatriya · doi:10.30629/2618-6667-2026-24-2-119-130

How does AI detect diabetic retinopathy from retinal photos? A heatmap analysis of 54 deep learning modelsPreprint

Murphy, T. I., & Armitage, J. A. (2026) · medRxiv · doi:10.64898/2026.06.19.26356040

An interpretable clinical-proteomic framework identifies baseline neuroinflammatory proteomic patterns associated with post-thrombectomy mortality

Xiao, M., Qiao, N., 张瑞红, Xi, R., Ma, X. C., Li, H., Zhou, H.-Q., Liu, Y., & Lin, J. (2026) · Journal of Neuroinflammation · doi:10.1186/s12974-026-03971-4

MRI radiomics combined with clinical features for predicting initial dose in focused ultrasound ablation surgery for uterine fibroids

Zhao, L., Li, G., Chen, Q., He, M., & Zhang, Z. (2026) · International Journal of Hyperthermia · doi:10.1080/02656736.2026.2719927

AI-Powered Diabetic Retinopathy Screening

Mathenge, W. C., & Asaminew, T. (2026) · Springer eBooks · doi:10.1007/978-3-032-24854-1_20

Revolutionising sepsis management: A comprehensive review of artificial intelligence applications

Ahmad, S., Fatima, G., Raza, A. M., & KHAN, S. K. (2026) · Scripta Medica · doi:10.5937/scriptamed57-61970

Lightweight structure-aware explanations for rule-based models: application to radiotherapy risk communication in breast cancer

Umbrico, A., Bologna, G., Coraci, L., Fracasso, F., Gola, S., & Cortellessa, G. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1925421

Rethinking Objectivity in Clinical AI: A Qualitative Study of Concussion Evaluators

Zaidi, A. M., Saw, J. J.-W., Fu, L., Arneson, K., Kim, I., Cross, A., & Karahalios, K. G. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816992

Interpretable Machine Learning Model for Predicting Prior Authorization Approval Delays Using Payer-Specific Rules, Clinical Documentation Features, Procedure Type, Medical Necessity Indicators, and Historical Approval Timelines

Nam, N. V., Hoa, T. T., & Duc, L. M. (2025) · Journal of Health Informatics and Digital Systems · doi:10.68159/o275241982

Intraoperative indocyanine green fluorescence imaging and multimodal deep learning for predicting non-salvage outcomes in pediatric testicular torsion

Fang, Z., Wang, Z., Tu, X., Zhou, Y., Zhang, T., Fu, M., Huang, M., Zhang, L., Cao, X., Yan, X., et al. (2026) · Frontiers in Pediatrics · doi:10.3389/fped.2026.1936322

Inteligencia Artificial y medicina: la necesidad de modelos interpretables

Lumbreras, S. (2021) · TECHNO REVIEW International Technology Science and Society Review /Revista Internacional de Tecnología Ciencia y Sociedad · doi:10.37467/gka-revtechno.v9.2814 · Cited by 3

Artificial intelligence and medicine: The need for interpretable models

Lumbreras, S. (2020)

Application of Artificial Intelligence in Healthcare: The Need for More Interpretable Artificial Intelligence

Tavares, J. (2024) · Acta Médica Portuguesa · doi:10.20344/amp.20469 · Cited by 4

The need for interpretable and explainable deep learning data in health care

Badar, M. S., Arshad, M., Badar, L. R., Kumar, J., Anilkumar, J. P., Siddiqui, S., Alamoudi, O. A., & Mehare, H. B. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44915-4.00003-4

Explainable AI for Healthcare: From Black Box to Interpretable Models

Adadi, A., & Berrada, M. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-981-15-0947-6_31 · Cited by 137