The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

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Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

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Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

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Sleep Apnea Detection From Single-Lead ECG: A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms

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A Stable AI-Based Binary and Multiple Class Heart Disease Prediction Model for IoMT

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FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies

Ebrahimian, S., Kalra, M. K., Agarwal, S., Bizzo, B. C., Elkholy, M., Wald, C., Allen, B., & Dreyer, K. J. (2022) · Academic Radiology · doi:10.1016/j.acra.2021.09.002 · Cited by 120

Integrating AI into radiology workflow: levels of research, production, and feedback maturity

Dikici, E., Bigelow, M. T., Prevedello, L. M. S., White, R., & Erdal, B. S. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.1.016502 · Cited by 119

Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM

Zheng, R., Wang, Q., Lv, S., Li, C., Wang, C., Chen, W., & Wang, H. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3175461 · Cited by 118

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

Atkinson, R. D. (2019) · Cited by 115

A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions

Yang, Y. S., Li, Z., Du, M., Bo, J., Liu, H., Ren, L., Li, X., & Deen, M. J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104887 · Cited by 114

Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care

Adus, S., Macklin, J. A., & Pinto, A. D. (2023) · BMC Health Services Research · doi:10.1186/s12913-023-10098-2 · Cited by 111

Equity within AI systems: What can health leaders expect?

Gurevich, E., Hassan, B. E., & Morr, C. E. (2022) · Healthcare Management Forum · doi:10.1177/08404704221125368 · Cited by 110

Clinical applications of artificial intelligence in sleep medicine: A sleep clinician’s perspective

Bandyopadhyay, A., & Goldstein, C. (2023) · Sleep And Breathing · doi:10.1007/s11325-022-02592-4 · Cited by 108

Robotics and AI for Teleoperation, Tele-Assessment, and Tele-Training for Surgery in the Era of COVID-19: Existing Challenges, and Future Vision

Feizi, N., Tavakoli, M., Patel, R. V., & Atashzar, S. F. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.610677 · Cited by 106

Artificial intelligence-assisted reduction in patients’ waiting time for outpatient process: a retrospective cohort study

Li, X., Tian, D., Li, W., Dong, B., Wang, H., Yuan, J., Li, B., Shi, L., Lin, X., Zhao, L., et al. (2021) · BMC Health Services Research · doi:10.1186/s12913-021-06248-z · Cited by 103

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Fusco, R., Grassi, R., Granata, V., Setola, S. V., Grassi, F., Cozzi, D., Pecori, B., Izzo, F., & Petrillo, A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11100993 · Cited by 99

A Machine Learning Approach to Predicting Diabetes Complications

Jian, Y., Pasquier, M., Sagahyroon, A. A., & Aloul, F. (2021) · Healthcare · doi:10.3390/healthcare9121712 · Cited by 98

Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review

Knight, D. R. T., Aakre, C. A., Anstine, C. V., Munipalli, B., Biazar, P., Mitri, G., Valery, J. R., Brigham, T., Niazi, S. K., Perlman, A. I., et al. (2023) · Health Policy and Technology · doi:10.1016/j.hlpt.2023.100824 · Cited by 98

An Artificial Intelligence‐Based Chatbot for Prostate Cancer Education: Design and Patient Evaluation Study

Görtz, M., Baumgärtner, K., Schmid, T., Muschko, M., Woessner, P., Gerlach, A., Byczkowski, M., Sültmann, H., Duensing, S., & Hohenfellner, M. (2023) · DIGITAL HEALTH · doi:10.1177/20552076231173304 · Cited by 96

Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography

Watanabe, A. T., Lim, V., Vu, H., Chim, C. Y., Weise, E., Liu, J., Bradley, W. G., & Comstock, C. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00192-5 · Cited by 96

Requirements and reliability of AI in the medical context

Balagurunathan, Y., Mitchell, R., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.024 · Cited by 96

Artificial Intelligence Can Improve Patient Management at the Time of a Pandemic: The Role of Voice Technology

Jadczyk, T., Wojakowski, W., Tendera, M., Henry, T. D., Egnaczyk, G. F., & Shreenivas, S. (2021) · Journal of Medical Internet Research · doi:10.2196/22959 · Cited by 95

Cross-Validation With Confidence

Lei, J. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1672556 · Cited by 93

Combining the power of artificial intelligence with the richness of healthcare claims data: Opportunities and challenges

Thesmar, D., Sraer, D., Pinheiro, L., Dadson, N., Veliche, R., & Greenberg, P. (2019) · PharmacoEconomics · doi:10.1007/s40273-019-00777-6 · Cited by 91

Artificial Intelligence Predictive Analytics in the Management of Outpatient MRI Appointment No-Shows

Chong, L. R., Tsai, K. T., Lee, L. L., Foo, S. G., & Chang, P. C. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.19.22594 · Cited by 87

The European Legal Framework for Medical AI

Schneeberger, D., Stöger, K., & Holzinger, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-57321-8_12 · Cited by 85

A Recommendation System for Proactive Health Monitoring Using IoT and Wearable Technologies

Asthana, S., Megahed, A., & Strong, R. (2017) · doi:10.1109/aims.2017.11 · Cited by 81

Applications of artificial intelligence for nursing: has a new era arrived?

Bulck, L. V., Couturier, R., & Moons, P. (2022) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvac097 · Cited by 81

Informed consent for artificial intelligence in emergency medicine: A practical guide

Iserson, K. V. (2023) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2023.11.022 · Cited by 80

Deep learning-based NLP data pipeline for EHR-scanned document information extraction

Hsu, E., Malagaris, I., Kuo, Y., Sultana, R., & Roberts, K. E. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac045 · Cited by 75

Automated diagnosis of multi-class brain abnormalities using MRI images: A deep convolutional neural network based method

Nayak, D. R., Dash, R., & Majhi, B. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.04.018 · Cited by 74

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

Schalekamp, S., Klein, W. M., & van Leeuwen, K. G. (2021) · Pediatric Radiology · doi:10.1007/s00247-021-05146-0 · Cited by 72

Unsupervised ECG Analysis: A Review

Nezamabadi, K., Sardaripour, N., Haghi, B., & Forouzanfar, M. (2022) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3154893 · Cited by 72

COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring

Frid-Adar, M., Amer, R., Gozes, O., Nassar, J., & Greenspan, H. K. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3069169 · Cited by 70

A combined rule-based and machine learning approach for automated GDPR compliance checking

Hamdani, R. E., Mustapha, M., Amariles, D. R., Troussel, A., Meeùs, S., & Krasnashchok, K. (2021) · International Conference on Artificial Intelligence and Law · doi:10.1145/3462757.3466081 · Cited by 69

AI and privacy concerns: a smart meter case study

Carmody, J., Shringarpure, S., & de Venter, G. V. (2021) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-04-2021-0042 · Cited by 69

AI-enhanced breast imaging: Where are we and where are we heading?

Bitencourt, A. G. V., Naranjo, I. D., Gullo, R. L., Saccarelli, C. R., & Pinker, K. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109882 · Cited by 69

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation

Klement, W., & Emam, K. E. (2023) · Journal of Medical Internet Research · doi:10.2196/48763 · Cited by 69

A clinical specific BERT developed using a huge Japanese clinical text corpus

Kawazoe, Y., Shibata, D., Shinohara, E., Aramaki, E., & Ohe, K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259763 · Cited by 65

Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center

Mandelbaum, A., Momčilović, P., Trichakis, N., Kadish, S., Leib, R., & Bunnell, C. A. (2019) · Management Science · doi:10.1287/mnsc.2018.3218 · Cited by 64

Prescriptive analytics for inventory management in health care

Galli, L., Levato, T., Schoen, F., & Tigli, L. (2020) · Journal of the Operational Research Society · doi:10.1080/01605682.2020.1776167 · Cited by 63

Machine learning in patient flow: a review

El-Bouri, R., Taylor, T., Youssef, A., Zhu, T., & Clifton, D. A. (2021) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/abddc5 · Cited by 63

Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer

Hoogendoorn, M., Szolovits, P., Moons, L. M. G., & Numans, M. E. (2016) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2016.03.003 · Cited by 62

A machine learning approach for individual claims reserving in insurance

Baudry, M., & Robert, C. Y. (2019) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.2455 · Cited by 62

Using artificial intelligence to support compliance with the general data protection regulation

Kingston, J. (2017) · Artificial Intelligence and Law · doi:10.1007/s10506-017-9206-9 · Cited by 60

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Fan, Y., Long, E., Cai, L., Cao, Q., Wu, X., & Tong, R. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.665951 · Cited by 58

Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder

Bartsch, G. C., Mitra, A. P., Mitra, S. A., et al. (2015) · The Journal of Urology · doi:10.1016/j.juro.2015.09.090 · Cited by 58

Hardware Architectures for Real-Time Medical Imaging

Alcaín, E., Fernández, P. R., Nieto, R., Montemayor, A. S., Vilas, J., Galiana-Bordera, A., Martínez-Gironés, P. M., Prieto-de-la-Lastra, C., Rodríguez-Vila, B., Sanz, M. B., et al. (2021) · Electronics · doi:10.3390/electronics10243118 · Cited by 57

Act Like a Radiologist: Towards Reliable Multi-View Correspondence Reasoning for Mammogram Mass Detection

Liu, Y., Zhang, F., Chen, C., Wang, S., Wang, Y., & Yu, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3085783 · Cited by 55

Integrated failure analysis using machine learning predictive system for smart management of medical equipment maintenance

Zamzam, A. H., Hasikin, K. B., & Wahab, A. K. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106715 · Cited by 54

Collaboration and coordination strategies for a multi-level AI-enabled healthcare supply chain under disaster

Adhikari, A., Joshi, R., & Basu, S. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2252933 · Cited by 54

Understanding How the Design and Implementation of Online Consultations Affect Primary Care Quality: Systematic Review of Evidence With Recommendations for Designers, Providers, and Researchers

Darley, S., Coulson, T., Peek, N., Moschogianis, S., van der Veer, S. N., Wong, D. C., & Brown, B. C. (2022) · Journal of Medical Internet Research · doi:10.2196/37436 · Cited by 53

The influence of artificial intelligence techniques on disruption management: Does supply chain dynamism matter?

Samadhiya, A., Yadav, S., Kumar, A., V, Majumdar, A., Luthra, S., Garza‐Reyes, J. A., & Upadhyay, A. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102394 · Cited by 52

Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape

Djanian, S., Bruun, A., & Nielsen, T. D. (2022) · Sleep Medicine · doi:10.1016/j.sleep.2022.09.004 · Cited by 49

Energy efficiency in edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification

Corral, J. M. R., Civit-Masot, J., Luna-Perejón, F., Díaz-Cano, I., Morgado‐Estévez, A., & Domínguez-Morales, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107298 · Cited by 47

The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review

Brereton, T. A., Malik, M. M., Lifson, M. A., Greenwood, J. D., Peterson, K. J., & Overgaard, S. M. (2023) · Interactive Journal of Medical Research · doi:10.2196/45903 · Cited by 46

Beyond Supervised Learning for Pervasive Healthcare

Gu, X., Deligianni, F., Han, J., Liu, X., Chen, W., Yang, G., & Lo, B. P. L. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3296938 · Cited by 44

Building an AI That Feels: AI systems with emotional intelligence could learn faster and be more helpful

Czerwinski, M. P., Hernandez, J., & McDuff, D. (2021) · IEEE Spectrum · doi:10.1109/mspec.2021.9423818 · Cited by 43

A Radiology-focused Review of Predictive Uncertainty for AI Interpretability in Computer-assisted Segmentation

McCrindle, B. W., Zukotynski, K., Doyle, T. E., & Noseworthy, M. D. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021210031 · Cited by 43

Real-Time Capacity Management and Patient Flow Optimization in Hospitals Using AI Methods

Munavalli, J. R., Boersma, H. J., Rao, S. V., & van Merode, G. G. (2020) · Springer eBooks · doi:10.1007/978-3-030-45240-7_3 · Cited by 41

Medical Image Classification Using Generalized Zero Shot Learning

Mahapatra, D., Bozorgtabar, B., & Ge, Z. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00373 · Cited by 41

Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy

Lee, M., O'Hara, N. B., Sonoda, M., Kuroda, N., Juhász, C., Asano, E., Dong, M., & Jeong, J. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2977531 · Cited by 40

Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment

Lee, R. Y., Wu, Y., Goh, D. L. M., Tan, V., Ng, C. W., Lim, J. C. T., Lau, M. C., & Yeong, J. P.-S. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202202457 · Cited by 40

Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging

Sveric, K. M., Ulbrich, S., Dindane, Z., Winkler, A., Botan, R., Mierke, J., Trausch, A., Heidrich, F. M., & Linke, A. H.-P. (2023) · International Journal of Cardiology · doi:10.1016/j.ijcard.2023.131383 · Cited by 39

Patient Data-Sharing for AI: Ethical Challenges, Catholic Solutions

Baric-Parker, J., & Anderson, E. E. (2020) · The Linacre Quarterly · doi:10.1177/0024363920922690 · Cited by 38

AI-Powered Health Chatbots: Toward a general architecture

Khadija, A., Zahra, F. F., & Naceur, A. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.07.048 · Cited by 36

Machine Learning and Medical Appointment Scheduling: Creating and Perpetuating Inequalities in Access to Health Care

Samorani, M., & Blount, L. G. (2020) · American Journal of Public Health · doi:10.2105/ajph.2020.305570 · Cited by 35

Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG

Bahrami, M., & Forouzanfar, M. (2022) · Cardiovascular Engineering and Technology · doi:10.1007/s13239-022-00615-5 · Cited by 35

Adaptive dynamic programming algorithms for sequential appointment scheduling with patient preferences

Wang, J., & Fung, R. Y. K. (2014) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.12.002 · Cited by 34

Wearable on-device deep learning system for hand gesture recognition based on FPGA accelerator

Jiang, W., Xuelin, Y., Chen, R., Su, F., Lin, M., Ma, Y., Zhu, Y., & Huang, S. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021007 · Cited by 27

AI in Combating the COVID-19 Pandemic

Cao, L. (2022) · IEEE Intelligent Systems · doi:10.1109/mis.2022.3164313 · Cited by 22

Machine Learning for Prediction of Wait Times in Outpatient Clinic

Joseph, J., Senith, S., Kirubaraj, A. A., & Ramson, S. R. J. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.026 · Cited by 20

Real-world patient data: Can they support decision making and patient engagement?

Joeris, A., ZHU, T. Y., Lambert, S. M., Wood, A., & Jayakumar, P. (2021) · Injury · doi:10.1016/j.injury.2021.12.012 · Cited by 19

Artificial intelligence technologies empowering identification of novel diagnostic molecular markers in gastric cancer

Misra, V., Pandey, I., Pandey, A. T., Ramteke, P. W., & Agrawal, R. (2021) · Indian Journal of Pathology and Microbiology · doi:10.4103/ijpm.ijpm_950_20 · Cited by 16

Measuring outcomes in healthcare economics using Artificial Intelligence: With application to resource management

Huang, C., Batarseh, F. A., Boueiz, A. R., Kulkarni, A., Su, P.-H., & Aman, J. (2021) · Data & Policy · doi:10.1017/dap.2021.29 · Cited by 15

Prevent Medical Errors through Artificial Intelligence: A Review

Ahmad, S., & Wasim, S. (2023) · Saudi Journal of Medical and Pharmaceutical Sciences · doi:10.36348/sjmps.2023.v09i07.007 · Cited by 14

Intelligent Oscillometric System for Automatic Detection of Peripheral Arterial Disease

Forghani, N., Maghooli, K., Dabanloo, N. J., Vasheghani‐Farahani, A., & Forouzanfar, M. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3065379 · Cited by 12

Pragmatic Approaches to Interoperability – Surmounting Barriers to Healthcare Data and Information Across Organizations and Political Boundaries

Perugu, B., Wadhwa, B. V., Kim, M. J., Cai, B. J., Shin, B. A., & Gupta, A. D. (2023) · Telehealth and Medicine Today · doi:10.30953/thmt.v8.421 · Cited by 12

Human-AI Collaboration to Promote Trust, Engagement and Adaptation in the Process of Pro-environmental and Health Behaviour Change

Puerta-Beldarrain, M., Gómez–Carmona, O., Casado–Mansilla, D., & López–de–Ipiña, D. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-21333-5_38 · Cited by 10

A Low Memory Requirement MobileNets Accelerator Based on FPGA for Auxiliary Medical Tasks

Lin, Y.-R., Zhang, Y., & Yang, X. (2022) · Bioengineering · doi:10.3390/bioengineering10010028 · Cited by 10

Unifying Domain Adaptation and Domain Generalization for Robust Prediction Across Minority Racial Groups

Khoshnevisan, F., & Chi, M. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-86486-6_32 · Cited by 9

Accountability, Transparency and Explainability in AI for Healthcare

Kempton, A. M., & Vassilakopoulou, P. (2021) · Gesellschaft für Informatik (GI) · doi:10.18420/ihc2021_018 · Cited by 9

Determinación del grado de agresividad tumoral mediante técnicas de inteligencia artificial aplicadas a imágenes de resonancia magnética

Segura, F., Segura, F. P., & Segura, F. V. (2023) · Revista de la Asociación Argentina de Ortopedia y Traumatología · doi:10.15417/issn.1852-7434.2023.88.6.1738 · Cited by 8

Measurement of Heart Rate and Heart Rate Variability with Wearable Devices: A Systematic Review

Stangl, F. J., & Riedl, R. (2022) · Journal of the Association for Information Systems · Cited by 8

AI in Longevity

Galkin, F., & Zhavoronkov, A. A. (2023) · Healthy ageing and longevity · doi:10.1007/978-3-031-35176-1_1 · Cited by 3

A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges

Mienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101587 · Cited by 212

Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency

Bhuyan, S. S., Sateesh, V., Mukul, N., Galvankar, A., Mahmood, A., Nauman, M., Rai, A., Bordoloi, K., Basu, U., & Samuel, J. (2025) · Journal of Medical Systems · doi:10.1007/s10916-024-02136-1 · Cited by 145

The integration of AI in nursing: addressing current applications, challenges, and future directions

Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1545420 · Cited by 143

Artificial intelligence in nursing: an integrative review of clinical and operational impacts

Hassanein, S., El Arab, R. A., Abdrbo, A., Abu-Mahfouz, M. S., Gaballah, M. K. F., Seweid, M. M., Almari, M., & Alzghoul, H. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1552372 · Cited by 134

Artificial intelligence and neuroscience: transformative synergies in brain research and clinical applications

Onciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113