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

Toward governance of artificial intelligence in pediatric healthcare

Richter, F., Holmes, E., Richter, F., Guttmann, K., Duong, S. Q., Gangadharan, S., Schadt, E., Salmasian, H., Gelb, B. D., & Glicksberg, B. S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02000-7 · Cited by 27

DeepGeni: deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy

Oh, M., & Zhang, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-31210-w · Cited by 22

Explainable Machine Learning Methods for Classification of Brain States during Visual Perception

Islam, R., Andreev, A. V., Shusharina, N. N., & Hramov, A. E. (2022) · Mathematics · doi:10.3390/math10152819 · Cited by 19

A systematic review of machine learning applications in predicting opioid associated adverse events

Medina, C. R. R., Benitez-Aurioles, J., Jenkins, D. A., & Jani, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01312-4 · Cited by 17

Digital Biomarkers for Precision Early Detection of Lung Cancer: Integrating AI ‐Driven Multi‐Omics Into Clinical Pathways

Bu, F., & Ling, Z. (2026) · Cancer Medicine · doi:10.1002/cam4.71578 · Cited by 18

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

Bugeac, C. A., Ancuceanu, R., & Dinu, M. (2021) · Molecules · doi:10.3390/molecules26061734 · Cited by 17

Explainable multimodal machine learning model for classifying pregnancy drug safety

Shtar, G., Rokach, L., Shapira, B., Kohn, E., Berkovitch, M., & Berlin, M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab769 · Cited by 17

Review on Advent of Artificial Intelligence in Electrocardiogram for the Detection of Extra-Cardiac and Cardiovascular Disease

Joy, S. I., Kumar, K. S., Palanivelan, M., & Lakshmi, D. (2023) · Canadian Journal of Electrical and Computer Engineering · doi:10.1109/icjece.2022.3228588 · Cited by 22

Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging

Alahdab, F., Shawi, R. E., Ahmed, A. I., Han, Y., & Al-Mallah, M. H. (2023) · PLoS ONE · doi:10.1371/journal.pone.0291451 · Cited by 16

Multi-stain deep learning prediction model of treatment response in lupus nephritis based on renal histopathology

Cheng, C., Li, B., Li, J., Wang, Y., Xiao, H., Lian, X., Chen, L., Wang, J., Wang, H., Qin, S., et al. (2024) · Kidney International · doi:10.1016/j.kint.2024.12.007 · Cited by 20

Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study

Xiao, N., Huang, X., Wu, Y., Li, B., Zang, W., Shinwari, K., Тузанкина, И. А., Черешнев, В. А., & Liu, G. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1523902 · Cited by 20

Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records

Hur, C., Wi, J., & Kim, Y. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17228303 · Cited by 17

Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm

Hwang, E., Park, H., Kim, H.-S., Kim, J.-Y., Jeong, H., Kim, J., & Kim, S.-H. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102569 · Cited by 15

Clinical Characteristics of Neonatal Cholestasis in a Tertiary Hospital and the Development of a Novel Prediction Model for Mortality

Choi, H. J., Kim, I., Lee, H., Oh, H. J., Ahn, M. K., Baek, W. I., Kim, Y. E., Oh, S. H., Lee, B. S., Namgoong, J., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2022.103890 · Cited by 15

Deep learning for risk-based stratification of cognitively impaired individuals

Romano, M. F., Zhou, X., Balachandra, A. R., Jadick, M. F., Qiu, S., Nijhawan, D. A., Joshi, P. S., Mohammad, S., Lee, P. H., Smith, M. J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107522 · Cited by 13

Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry

Stefano, V. D., D’Angelo, M., Monaco, F., Vignapiano, A., Martiadis, V., Barone, E., Fornaro, M., Steardo, L., Solmi, M., Manchia, M., et al. (2024) · Brain Sciences · doi:10.3390/brainsci14121196 · Cited by 20

Augmenting Osteoporosis Imaging with Machine Learning

Pedoia, V., Calivá, F., Kazakia, G. J., Burghardt, A. J., & Majumdar, S. (2021) · Current Osteoporosis Reports · doi:10.1007/s11914-021-00701-y · Cited by 14

Deep learning applications in vascular dementia using neuroimaging

Dong, C., & Hayashi, S. (2024) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000920 · Cited by 13

Interpretable Policies and the Price of Interpretability in Hypertension Treatment Planning

Garcia, G. P., Steimle, L. N., Marrero, W. J., & Sussman, J. B. (2023) · Manufacturing & Service Operations Management · doi:10.1287/msom.2021.0373 · Cited by 13

Importance-aware personalized learning for early risk prediction using static and dynamic health data

Tan, Q., Ye, M., Ma, A. J., Yip, T. C., Wong, G. L., & Yuen, P. C. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa306 · Cited by 14

Enhancing Risk Stratification on Coronary Computed Tomography Angiography: The Role of Artificial Intelligence

Jaltotage, B., Sukudom, S., Ihdayhid, A. R., & Dwivedi, G. (2023) · Clinical Therapeutics · doi:10.1016/j.clinthera.2023.09.019 · Cited by 13

Risk prediction algorithms and clinical judgment: Impact of advice distance, social proof, and feature-importance explanations

Pálfi, B., Arora, K., Prociuk, D., & Kostopoulou, O. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108102 · Cited by 14

Deep propensity network using a sparse autoencoder for estimation of treatment effects

Ghosh, S., Bian, J., Guo, Y. M., & Prosperi, M. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa346 · Cited by 10

Comparison and interpretability of machine learning models to predict severity of chest injury

Kulshrestha, S., Dligach, D., Joyce, C., Gonzalez, R., O’Rourke, A. P., Glazer, J. M., Stey, A. M., Kruser, J. M., Churpek, M. M., & Afshar, M. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab015 · Cited by 12

Applying Artificial Intelligence and machine learning in precision nutrition

Massara, P., Kirkland, J., Pagani, I., Huey, S. L., Hirsh, H., McDonald, D. T., Patel, L., Finkelstein, J. L., Gantz, M. G., Wang, F., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-75004-w · Cited by 15

LIME-Enabled Investigation of Convolutional Neural Network Performances in COVID-19 Chest X-Ray Detection

Cervantes, E. G., & Chan, W.-Y. (2021) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece53047.2021.9569029 · Cited by 12

Artificial intelligence for gastroenterology: Singapore artificial intelligence for Gastroenterology Working Group Position Statement

Sung, J. J. �., Savulescu, J., Ngiam, K. Y., An, B., Ang, T. L., Yeoh, K. G., Cham, T., Tsao, S. K. K., & Chua, T. (2023) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.16241 · Cited by 13

The Promise of Artificial Intelligence-Assisted Point-of-Care Ultrasonography in Perioperative Care

Serrano, R. A., & Smeltz, A. M. (2024) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2024.01.034 · Cited by 11

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis

Chang, C., Shi, W., Wang, Y., Zhang, Z., Huang, X., & Jiao, Y. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108258 · Cited by 15

Artificial Intelligence in Imaging in the First Trimester of Pregnancy: A Systematic Review

Umans, E., Dewilde, K., Williams, H., Deprest, J. A., & den Bosch, T. V. (2024) · Fetal Diagnosis and Therapy · doi:10.1159/000538243 · Cited by 12

Artificial Intelligence in Diabetes Management: Revolutionizing the Diagnosis of Diabetes Mellitus; a Literature Review

Keshtkar, A., Ayareh, N., Atighi, F., Hamidi, R., Yazdanpanahi, P., Karimi, A., Naseri, A., Hosseini, F. S., & Dabbaghmanesh, M. H. (2024) · Shiraz E-Medical Journal · doi:10.5812/semj-146903 · Cited by 11

Current Status and Future Direction of Artificial Intelligence in Healthcare and Medical Education

Jung, J. S. (2020) · Korean Medical Education Review · doi:10.17496/kmer.2020.22.2.99 · Cited by 13

Voxel‐based diktiometry: Combining convolutional neural networks with voxel‐based analysis and its application in diffusion tensor imaging for Parkinson's disease

Estudillo-Romero, A., Haegelen, C., Jannin, P., & Baxter, J. S. H. (2022) · Human Brain Mapping · doi:10.1002/hbm.26009 · Cited by 11

Clinical Validation of a Machine Learning-Based Biomarker Signature to Predict Response to Cytotoxic Chemotherapy Alone or Combined with Targeted Therapy in Metastatic Colorectal Cancer Patients: A Study Protocol and Review

Pagano, D., Barresi, V., Tropea, A., Galvano, A., Bazan, V., Caldarella, A., Sani, C., Pompeo, G., Russo, V., Liotta, R., et al. (2025) · Life · doi:10.3390/life15020320 · Cited by 10

Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation

Honarvar, H., Agarwal, C., Somani, S. S., Vaid, A., Lampert, J. M., Wanyan, T., Reddy, V. Y., Nadkarni, G. N., Miotto, R., Žitnik, M., et al. (2022) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2022.07.074 · Cited by 9

Core Concepts in Pharmacoepidemiology: Principled Use of Artificial Intelligence and Machine Learning in Pharmacoepidemiology and Healthcare Research

Rough, K., Rashidi, E., Tai, C. G.-L., Lucia, R. M., Mack, C. D., & Largent, J. A. (2024) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.70041 · Cited by 11

Explainability of a Deep Learning-Based Classification Model for Antineutrophil Cytoplasmic Autoantibody–Associated Glomerulonephritis

Trejo, M. A. C. W., Sadeghi, M., Singh, S., Mahmoodian, N., Sharifli, S., Hrušková, Z., Tesař, V., Puéchal, X., BRUIJN, J. A., Göbel, G., et al. (2024) · Kidney International Reports · doi:10.1016/j.ekir.2024.11.005 · Cited by 9

Prognostic Value of CD8+ T Cells at the Invasive Margin Is Comparable to the Immune Score in Nonmetastatic Colorectal Cancer: A Prospective Multicentric Cohort Study

Wankhede, D., Halama, N., Kloor, M., Edelmann, D., Brenner, H., & Hoffmeister, M. (2025) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-3275 · Cited by 9

Multimodal fusion architectures for Alzheimer’s disease diagnosis: An experimental study

Leony, F., & Lin, C. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104834 · Cited by 9

Responsible scaling of artificial intelligence in healthcare: standardization meets customization

Lukkien, D., Nap, H. H., Peine, A., Minkman, M. M. N., Moors, E. H. M., & Boon, W. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09842-5 · Cited by 9

Machine learning for prediction of postoperative complications after hepato-biliary and pancreatic surgery

Shapey, I. M., & Sultan, M. (2023) · Artificial Intelligence Surgery · doi:10.20517/ais.2022.31 · Cited by 9

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Yan, G., Gaowen, Y., Li, H., Liang, H., Peng, C., Bhetuwal, A., McClure, M. A., Li, Y., Yang, G., Li, Y., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.922299 · Cited by 9

Design of a Human-in-the-Loop Centered AI-Based Clinical Decision Support System for Professional Care Planning

Steffny, L., Dahlem, N., Reichl, L., Gisa, K., Greff, T., & Werth, D. (2023) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/faia230090 · Cited by 8

A way forward for responsibility in the age of AI

Gogoshin, D. L. (2025) · Inquiry · doi:10.1080/0020174x.2024.2312455 · Cited by 9

Ethical and social issues in prediction of risk of severe mental illness: a scoping review and thematic analysis

Neiders, I., Mežinska, S., & van Haren, N. E. M. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06949-3 · Cited by 10

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Patterson, A., Elbasir, A., Tian, B., & Auslander, N. (2023) · Cancers · doi:10.3390/cancers15071958 · Cited by 8

The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review

Zrubka, Z., Kertész, G., Gulàcsi, L., Czere, J. T., Hölgyesi, Á., Motahari-Nezhad, H., Mosavi, A. H., Kovács, L., Butte, A. J., & Péntek, M. (2023) · Journal of Medical Internet Research · doi:10.2196/47430 · Cited by 10

Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury

Jiang, M., Pan, C., Li, J., Xu, L., & Li, C.-L. (2023) · Renal Failure · doi:10.1080/0886022x.2022.2151468 · Cited by 7

Editorial commentary: Deep learning in cardiology: All models are wrong, but some are useful

Stultz, C. M. (2020) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2020.12.001 · Cited by 8

SyRACT: zero-shot biomedical document-level relation extraction with synergistic RAG and CoT

Dong, X., Zhao, D., Meng, J., Guo, B., & Lin, H. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf356 · Cited by 9

AI-Based Cancer Models in Oncology: From Diagnosis to ADC Drug Prediction

Sobhani, N., Kugeratski, F. G., Venturini, S., Roudi, R., Nguyen, T., D’Angelo, A., & Generali, D. (2025) · Cancers · doi:10.3390/cancers17213419 · Cited by 7

Leveraging Clinical Decision Support and Integrated Medical-Dental Electronic Health Records to Implementing Precision in Oral Cancer Risk Assessment and Preventive Intervention

Rindal, D. B., & Mabry, P. L. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090832 · Cited by 7

Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches

Liang, C., Pan, S., Wu, W., Chen, F., Zhang, C., Zhou, C., Gao, Y., Ruan, X., Quan, S., Zhao, Q., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.020 · Cited by 7

Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods

Hoebers, F. J. P., Wee, L., Likitlersuang, J., Mak, R. H., Bitterman, D. S., Huang, Y., Dekker, A. L. A. J., Aerts, H. J. W. L., & Kann, B. H. (2023) · BJR|Open · doi:10.1093/bjro/tzae039 · Cited by 9

Artificial intelligence/machine learning solutions for mobile and wearable devices

Xu, Z., Yu, B., & Wang, F. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820077-3.00004-3 · Cited by 8

Ethische, legale und soziale Implikationen bei der Anwendung künstliche-Intelligenz-gestützter Technologien in der Chirurgie

Wilhelm, D., Hartwig, R., McLennan, S., Arnold, S., Mildner, P., Feußner, H., Neumuth, T., & Bieck, R. (2022) · Der Chirurg · doi:10.1007/s00104-022-01574-2 · Cited by 7

Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future?

van der Kamp, A., Waterlander, T. J., de Bel, T., van der Laak, J. A. W. M., van den Heuvel‐Eibrink, M. M., Mavinkurve‐Groothuis, A. M. C., & de Krijger, R. R. (2022) · Pediatric and Developmental Pathology · doi:10.1177/10935266211059809 · Cited by 7

A guide to optometrists for appraising and using artificial intelligence in clinical practice

Murphy, T. I., Armitage, J. A., van Wijngaarden, P., Abel, L. A., & Douglass, A. G. (2023) · Clinical and Experimental Optometry · doi:10.1080/08164622.2023.2197578 · Cited by 7

Reducing bias in healthcare artificial intelligence: A white paper

Sun, C., & Harris, S. L. (2024) · Health Informatics Journal · doi:10.1177/14604582241291410 · Cited by 6

Artificial Intelligence in Tumor Evolution: Understanding Cancer Complexity Through Multi-Modal Data Integration in Precision Oncology

Espinosa-Sánchez, A., & Carnero, A. (2026) · Cells · doi:10.3390/cells15111031 · Cited by 11

Ethical frontiers in liver transplantation

Wong, H. P. N., Selvakumar, S. V., Loh, P. Y., Liau, J. Y. J., Liau, M. Y. Q., & Shelat, V. G. (2024) · World Journal of Transplantation · doi:10.5500/wjt.v14.i4.96687 · Cited by 7

Automated Identification of Surgical Candidates and Estimation of Postoperative Seizure Freedom in Children - A Focused Review

Samanta, D., Beal, J. C., & Grinspan, Z. M. (2021) · Seminars in Pediatric Neurology · doi:10.1016/j.spen.2021.100914 · Cited by 6

Designing Interpretable ML System to Enhance Trustworthy AI in Healthcare: A Systematic Review of the Last Decade to A Proposed Robust FrameworkPreprint

Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K. (2023) · Research Square · doi:10.21203/rs.3.rs-3626164/v2 · Cited by 6

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

de la Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020) · Journal of Alzheimer's Disease · doi:10.3233/jad-200888 · Cited by 5

Artificial intelligence and the delivery of patient-centered care in the Gulf region: navigating the ethical landscape

Alzaabi, A. A., & Padela, A. I. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23637-2.00022-9 · Cited by 7

Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View

VanBerlo, B., Smith, D., Tschirhart, J. N., VanBerlo, B., Wu, D., Ford, A., McCauley, J., Wu, B., Chaudhary, R., Dave, C., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12102351 · Cited by 5

Protecting the Pathology Commons in the Digital Era

Mazer, B. L., Paulson, N., & Sinard, J. H. (2020) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2020-0022-ed · Cited by 5

Artificial Intelligence to Guide Repurposing of Drugs

Fu, Z., Yang, Y., Chung, M. K., & Cheng, F. (2026) · Annual Review of Medicine · doi:10.1146/annurev-med-050224-122802 · Cited by 17

CSF-Glioma: A Causal Segmentation Framework for Accurate Grading and Subregion Identification of Gliomas

Zheng, Y., Huang, D. J., Feng, Y., Hao, X., He, Y., & Liu, Y. (2023) · Bioengineering · doi:10.3390/bioengineering10080887 · Cited by 4

A Systematic Review on ECG and EMG Biomedical Signal Using Deep-Learning Approaches

Chugh, A., & Jain, C. (2023) · The Springer series in applied machine learning · doi:10.1007/978-3-031-41925-6_11 · Cited by 4

Artificial intelligence for diabetes management – a review

Maheshwari, S., Kalia, A., Tewari, J., Tewari, A., Srivastava, A., Dantu, R., Sachan, A. K., Verma, N., & Maheshwari, A. (2025) · Journal of Diabetes Metabolic Disorders & Control · doi:10.15406/jdmdc.2025.12.00292 · Cited by 3

Patients, Doctors, and Chatbots

Erren, T. C. (2023) · JMIR Medical Education · doi:10.2196/50869 · Cited by 4

MOORA: Metode Sistem Pendukung Keputusan untuk Menentukan Kelayakan Peminjaman Modal Dana Bergulir

Ningsih, E. S., Syafwan, H., & Ihsan, M. (2023) · EDUMATIC Jurnal Pendidikan Informatika · doi:10.29408/edumatic.v7i1.12405 · Cited by 4

Legal Aspects of AI in the Biomedical Field. The Role of Interpretable Models

Gallese, C. (2024) · Wiley eBooks · doi:10.1002/9781119846567.ch15 · Cited by 4

Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGSPreprint

Fan, F., Liu, Q., Zee, J., Ozeki, T., Demeke, D., Yang, Y., Farris, A. B., Wang, B., Shah, M. P., Jacobs, J., et al. (2024) · medRxiv · doi:10.1101/2024.07.19.24310619 · Cited by 4

Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery

Han, H., Zhang, J., Wang, X., Ge, W., & Qu, J. Z. (2025) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-025-02016-9 · Cited by 3

Deep learning with explainability: Improving diagnostic accuracy and interpretability in ECG and MCG analysis

Jia, Y., Pei, H., Xu, D., Pang, J., Chen, Y., Shen, C., & Xiang, M. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108875 · Cited by 3

Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis

Kang, M., Yang, Z., Tian, Y., Li, D., Wang, Z., & Chen, L. (2025) · Discover Oncology · doi:10.1007/s12672-025-03970-6 · Cited by 3

Mining Electronic Health Records for Real-World Evidence

Zang, C., Pan, W., & Wang, F. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599566 · Cited by 3

Multilevel Correlation-Aware and Modal-Aware Graph Convolutional Network for Diagnosing Neurodevelopmental Disorders

Zuo, S., Li, Y., Qi, Y., & Liu, A. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3617348 · Cited by 3

SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis

Xing, N., Yeung, S. H., Cai, C., Ng, T. K., Wang, W., Yang, K., Yang, N., Zhang, M., Chen, G., & Ooi, B. C. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475176 · Cited by 3

Ethical issues of implementing artificial intelligence in medicine

Konkov, M. I. (2023) · Digital Diagnostics · doi:10.17816/dd430348 · Cited by 3

Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records

Chi, C.-Y., Moghadas-Dastjerdi, H., Winkler, A., Ao, S., Chen, Y.-P., Wang, L.-W., Su, P., Lin, W., Tsai, M., & Huang, C. (2023) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2409265 · Cited by 4

Application of artificial intelligence in the management of patients with renal dysfunction

Zhang, B., Jiang, X., Yang, J., Huang, J., Hu, C., Hong, Y., Ni, H., & Zhang, Z. (2024) · Renal Failure · doi:10.1080/0886022x.2024.2337289 · Cited by 3

Essentials for AI Research in Cardiology: Challenges and Mitigations

Jaltotage, B., & Dwivedi, G. (2024) · CJC Open · doi:10.1016/j.cjco.2024.07.015 · Cited by 5

Applications of AI Techniques in Healthcare and Wellbeing

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