Artificial Intelligence Transforms the Future of Health Care

Multiple imputation using chained equations: Issues and guidance for practice

White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792 · see citing works

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works

Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

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Refining Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation Using a Novel Risk Factor-Based Approach

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Artificial intelligence in healthcare: past, present and future

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · see citing works

Predicting the future - big data, machine learning, and clinical medicine

Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652 · see citing works

Machine Learning in Medicine

Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466 · see citing works

Machine Learning Applications in Cancer Prognosis and Prediction

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338 · see citing works

Spironolactone for heart failure with preserved ejection fraction

Pitt, B., Pfeffer, M. A., Assmann, S. F., Boineau, R., Anand, I. S., Claggett, B., Clausell, N., Desai, A. S., Diaz, R., Fleg, J. L., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1313731 · Cited by 2,809 · see citing works

Machine learning for medical imaging

Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017) · RadioGraphics · doi:10.1148/rg.2017160130 · Cited by 1,672 · see citing works

Pulmonary Hypertension in Heart Failure With Preserved Ejection Fraction

Lam, C. S. P., Roger, V. L., Rodeheffer, R. J., Borlaug, B. A., Enders, F., & Redfield, M. M. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2008.11.051 · Cited by 1,334 · see citing works

Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial

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A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM risk-SCD)

O'Mahony, C., Jichi, F., Pavlou, M., Monserrat, L., Anastasakis, A., Rapezzi, C., Biagini, E., Gimeno, J. R., Limongelli, G., McKenna, W. J., et al. (2014) · European Heart Journal · doi:10.1093/eurheartj/eht439 · Cited by 1,251 · see citing works

Artificial Intelligence in Precision Cardiovascular Medicine

Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.03.571 · Cited by 1,126 · see citing works

Applications of Machine Learning in Cancer Prediction and Prognosis

Cruz, J. A., & Wishart, D. S. (2006) · Cancer Informatics · doi:10.1177/117693510600200030 · Cited by 1,113 · see citing works

Using recurrent neural network models for early detection of heart failure onset

Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw112 · Cited by 928 · see citing works

Applications of machine learning in cancer prediction and prognosis.

Cruz, J. A., & Wishart, D. S. (2007) · PubMed Central · Cited by 879 · see citing works

Artificial intelligence in medical practice: the question to the answer?

Miller, D. D., & Brown, E. W. (2018) · The American Journal of Medicine · doi:10.1016/j.amjmed.2017.10.035 · Cited by 774 · see citing works

A community effort to assess and improve drug sensitivity prediction algorithms

Community, N. D., Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2877 · Cited by 761 · see citing works

What Is Data Ethics?

Floridi, L., & Taddeo, M. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2016.0360 · Cited by 637 · see citing works

Pulmonary Hypertension in Heart Failure With Preserved Ejection Fraction

Guazzi, M., Vicenzi, M., Arena, R. A., & Guazzi, M. D. (2011) · Circulation · doi:10.1161/circulationaha.110.983866 · Cited by 548 · see citing works

IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research

Chen, Y., Elenee Argentinis, J., & Weber, G. (2016) · Clinical Therapeutics · doi:10.1016/j.clinthera.2015.12.001 · Cited by 466 · see citing works

Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration

Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0015-z · Cited by 443 · see citing works

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works

Secondary Use of EHR: Data Quality Issues and Informatics Opportunities.

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2017 IEEE International Conference on Big Data (Big Data)

Jian-Yun Nie, Z. O., Nie, J.-Y., Zoran Obradovic, A., Suzumura, T., Ghosh, R., Nambiar, R., Wang, C., Zang, H., Baeza-Yates, R., Hu, X., et al. (2017) · doi:10.1109/bigdata41644.2017 · Cited by 302 · see citing works

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019) · Radiology · doi:10.1148/radiol.2018180694 · Cited by 294 · see citing works

Reproducible pharmacogenomic profiling of cancer cell line panels

Haverty, P. M., Lin, E., Tan, J., Yu, Y., Lam, B., Lianoglou, S., Neve, R. M., Martin, S. E., Settleman, J., Yauch, R. L., et al. (2016) · Nature · doi:10.1038/nature17987 · Cited by 279 · see citing works

Deep into the Brain: Artificial Intelligence in Stroke Imaging

Lee, E., Kim, Y., Kim, N., & Kang, D. (2017) · Journal of Stroke · doi:10.5853/jos.2017.02054 · Cited by 264 · see citing works

High-Definition Medicine

Torkamani, A., Andersen, K. G., Steinhubl, S. R., & Topol, E. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.007 · Cited by 245 · see citing works

Early and innovative interventions for severe sepsis and septic shock: taking advantage of a window of opportunity

Rivers, E. (2005) · Canadian Medical Association Journal · doi:10.1503/cmaj.050632 · Cited by 175 · see citing works

Predicting Survival From Large Echocardiography and Electronic Health Record Datasets

Samad, M. D., Ulloa, A., Wehner, G. J., Jing, L., Hartzel, D. N., Good, C. W., Williams, B. A., Haggerty, C. M., & Fornwalt, B. K. (2018) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.04.026 · Cited by 168 · see citing works

Supporting Regularized Logistic Regression Privately and Efficiently

Li, W., Liu, H., Yang, P., & Xie, W. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156479 · Cited by 162 · see citing works

Patient privacy in the era of big data

Kayaalp, M. (2018) · Balkan Medical Journal · doi:10.4274/balkanmedj.2017.0966 · Cited by 142 · see citing works

A Randomized Double-Blind Trial of Enalapril in Older Patients With Heart Failure and Preserved Ejection Fraction

Kitzman, D. W., Hundley, W. G., Brubaker, P. H., Morgan, T. M., Moore, J. B., Stewart, K. P., & Little, W. C. (2010) · Circulation Heart Failure · doi:10.1161/circheartfailure.109.898916 · Cited by 131 · see citing works

Novel Screening Tool for Stroke Using Artificial Neural Network

Abedi, V., Goyal, N., Tsivgoulis, G. K., Hosseinichimeh, N., Hontecillas, R., Bassaganya-Riera, J., Elijovich, L., Metter, J. E., Alexandrov, A. W., Liebeskind, D. S., et al. (2017) · Stroke · doi:10.1161/strokeaha.117.017033 · Cited by 124 · see citing works

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies

Huang, C., Mezencev, R., McDonald, J. F., & Vannberg, F. O. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186906 · Cited by 119 · see citing works

Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey?

Mehta, N., & Devarakonda, M. V. (2018) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2018.02.025 · Cited by 118 · see citing works

Heart Failure With Preserved Ejection Fraction

Udelson, J. E. (2011) · Circulation · doi:10.1161/circulationaha.111.071696 · Cited by 114 · see citing works

A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation

Smith, S. W., Walsh, B., Grauer, K., Wang, K., Rapin, J., Li, J., Fennell, W. H., & Taboulet, P. (2018) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2018.11.013 · Cited by 107 · see citing works

Decoding the Genomics of Abdominal Aortic Aneurysm

Li, J., Pan, C., Zhang, S., Spin, J. M., Deng, A. C., Leung, L. L. K., Dalman, R. L., Tsao, P. S., & Snyder, M. P. (2018) · Cell · doi:10.1016/j.cell.2018.07.021 · Cited by 95 · see citing works

Machine learning and genomics: precision medicine versus patient privacy

Azencott, C.-A. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2017.0350 · Cited by 89 · see citing works

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

Schnyer, D. M., Clasen, P. C., Gonzalez, C. E., & Beevers, C. G. (2017) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2017.03.003 · Cited by 70 · see citing works

Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost

Chen, Y., Wang, X., Jung, Y., Abedi, V., Zand, R., Bikak, M., & Adibuzzaman, M. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aadf0f · Cited by 68 · see citing works

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

Kesorn, K., Ongruk, P., Chompoosri, J., Phumee, A., Thavara, U., Tawatsin, A., & Siriyasatien, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125049 · Cited by 66 · see citing works

Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value

Elahian, B., Yeasin, M., Mudigoudar, B. D., Wheless, J. W., & Abbas Babajani-feremi, A. (2017) · Seizure · doi:10.1016/j.seizure.2017.07.010 · Cited by 66 · see citing works

Serendipity in anticancer drug discovery

Hargrave-Thomas, E. (2012) · World Journal of Clinical Oncology · doi:10.5306/wjco.v3.i1.1 · Cited by 49 · see citing works

In silico clinical trials for pediatric orphan diseases

Carlier, A., Vasilevich, A. S., Maréchal, M., de Boer, J., & Geris, L. (2018) · Scientific Reports · doi:10.1038/s41598-018-20737-y · Cited by 45 · see citing works

SecureMA: protecting participant privacy in genetic association meta-analysis

Xie, W., Kantarcıoğlu, M., Bush, W. S., Crawford, D. C., Denny, J. C., Heatherly, R. D., & Malin, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu561 · Cited by 43 · see citing works

Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach

Kagawa, R., Kawazoe, Y., Ida, Y., Shinohara, E., Tanaka, K., Imai, T., & Ohe, K. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816681584 · Cited by 41 · see citing works

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda.

Nyakarahuka, L., Ayebare, S., Mosomtai, G., Kankya, C., Lutwama, J. J., Mwiine, F. N., & Skjerve, E. (2017) · Europe PMC (PubMed Central) · doi:10.1371/currents.outbreaks.07992a87522e1f229c7cb023270a2af1 · Cited by 38 · see citing works

Modeling and Simulation as a Tool to Bridge Efficacy and Safety Data in Special Populations

Harnisch, L. O., Shepard, T., Pons, G., & Pasqua, O. E. D. (2013) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.6 · Cited by 37 · see citing works

Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection

Leber, A. J., Hontecillas, R., Abedi, V., Tubau‐Juni, N., Zoccoli-Rodriguez, V., Stewart, C., & Bassaganya-Riera, J. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.05.003 · Cited by 36 · see citing works

St. Jude Children’s Research Hospital

Morrison, K. (2015) · doi:10.4135/9781483345758.n537 · Cited by 29 · see citing works

Machine learning and medicine: book review and commentary

Koprowski, R., & Foster, K. R. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0449-9 · Cited by 28 · see citing works

Optimizing drug development in oncology by clinical trial simulation: Why and how?

Gal, J., Milano, G. A., Ferrero, J.-­., Saâda-Bouzid, E., Viotti, J., Chabaud, S., Gougis, P., Tourneau, C. L., Schiappa, R., Paquet, A. C., et al. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx055 · Cited by 27 · see citing works

Supervised learning methods in modeling of CD4+ T cell heterogeneity

Lu, P., Abedi, V., Mei, Y., Hontecillas, R., Hoops, S., Carbo, A., & Bassaganya-Riera, J. (2015) · BioData Mining · doi:10.1186/s13040-015-0060-6 · Cited by 22 · see citing works

Computer-Aided Drug Discovery

Lu, P., Bevan, D. R., Leber, A. J., Hontecillas, R., Tubau‐Juni, N., & Bassaganya-Riera, J. (2018) · Springer eBooks · doi:10.1007/978-3-319-73238-1_2 · Cited by 16 · see citing works

Phase III Placebo-Controlled, Randomized Clinical Trial With Synthetic Crohn's Disease Patients to Evaluate Treatment Response

Abedi, V., Lu, P., Hontecillas, R., Verma, M., Vess, G., Philipson, C. W., Carbo, A., Leber, A. J., Juni, N. T., Hoops, S., et al. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804203-8.00028-6 · Cited by 15 · see citing works

Development of Synthetic Patient Populations and In Silico Clinical Trials

Zand, R., Abedi, V., Hontecillas, R., Lu, P., Noorbakhsh-Sabet, N., Verma, M., Leber, A. J., Tubau‐Juni, N., & Bassaganya-Riera, J. (2018) · Springer eBooks · doi:10.1007/978-3-319-73238-1_5 · Cited by 15 · see citing works

Empirical study using network of semantically related associations in bridging the knowledge gap

Abedi, V., Yeasin, M., & Zand, R. (2014) · Journal of Translational Medicine · doi:10.1186/s12967-014-0324-9 · Cited by 14 · see citing works

Closing the loop — Finding lung cancer patients using NLP

Karunakaran, B., Misra, D., Marshall, K., Mathrawala, D., & Kethireddy, S. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258203 · Cited by 13 · see citing works

Temporal and Spatial Monitoring and Prediction of Epidemic Outbreaks

Zamiri, A., Yazdi, H. S., & Goli, S. A. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2338213 · Cited by 10 · see citing works

Accelerated Path to Cures

(2018) · Springer eBooks · doi:10.1007/978-3-319-73238-1 · Cited by 7 · see citing works

Latent-Based Imputation of Laboratory Measures from Electronic Health Records: Case for Complex DiseasesPreprint

Abedi, V., Shivakumar, M., Lu, P., Hontecillas, R., Leber, A. J., Ahuja, M., Ulloa, A., Shellenberger, M., & Bassaganya-Riera, J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/275743 · Cited by 6 · see citing works

Temporal progression in functional connectivity determines individual differences in working memory capacity

Bashivan, P., Yeasin, M., & Bidelman, G. M. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966220 · Cited by 5 · see citing works

Introduction to Accelerated Path to Cures and Precision Medicine in Inflammatory Bowel Disease

Bassaganya-Riera, J., & Hontecillas, R. (2018) · Springer eBooks · doi:10.1007/978-3-319-73238-1_1 · Cited by 3 · see citing works

Pathologists meet their match in tumour-spotting algorithm

(2018) · Nature · doi:10.1038/d41586-018-06724-3 · Cited by 1 · see citing works

Artificial intelligence literacy in higher and adult education: A scoping literature review

Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100101 · Cited by 658 · see citing works

Contributions and risks of artificial intelligence (AI) in building smarter cities: insights from a systematic review of the literature

Yigitcanlar, T., Desouza, K., Butler, L., & Roozkhosh, F. (2020) · Energies · doi:10.3390/en13061473 · Cited by 555 · see citing works

SHIFTing artificial intelligence to be responsible in healthcare: a systematic review

Siala, H., & Wang, Y. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114782 · Cited by 358 · see citing works

Generative AI in medical practice: in-depth exploration of privacy and security challenges

Chen, Y., & Esmaeilzadeh, P. (2024) · Journal of Medical Internet Research · doi:10.2196/53008 · Cited by 353 · see citing works

Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?

Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Feely, S. M. (2020) · AI · doi:10.3390/ai1020008 · Cited by 390 · 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

Using artificial intelligence to improve public health: a narrative review

Olawade, D. B., Wada, O. Z., David-Olawade, A. C., Kunonga, E., Abaire, O. J., & Ling, J. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1196397 · Cited by 321 · see citing works

Current status and applications of Artificial Intelligence (AI) in medical field: An overview

Haleem, A., Javaid, M., & Khan, I. H. (2019) · Current Medicine Research and Practice · doi:10.1016/j.cmrp.2019.11.005 · Cited by 267 · see citing works

Artificial intelligence and effective governance: A review, critique and research agenda

Sharma, G. D., Yadav, A., & Chopra, R. (2020) · Sustainable Futures · doi:10.1016/j.sftr.2019.100004 · Cited by 280 · see citing works

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients

Fritsch, S. J., Blankenheim, A., Wahl, A., Hetfeld, P., Maaßen, O., Deffge, S., Kunze, J., Rossaint, R., Riedel, M., Marx, G., et al. (2022) · Digital Health · doi:10.1177/20552076221116772 · Cited by 255 · see citing works

Responsible AI for Digital Health: a Synthesis and a Research Agenda

Trocin, C., Mikalef, P., Papamitsiou, Z., & Conboy, K. (2023) · Information Systems Frontiers · doi:10.1007/s10796-021-10146-4 · Cited by 205 · see citing works

Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review

Dixon, D., Sattar, H., Moros, N., Kesireddy, S. R., Ahsan, H., Lakkimsetti, M., Fatima, M., Doshi, D., Sadhu, K., & Hassan, M. J. (2024) · Cureus · doi:10.7759/cureus.59954 · Cited by 241 · see citing works

Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications

González, P. C., He, A., Lam, E. K. M., Ng, I. M. C., Li, M. W., Hou, R., Chan, J. N.-M., Sahni, Y., Viñas‐Guasch, N., Miller, T., et al. (2025) · Psychological Medicine · doi:10.1017/s0033291724003295 · Cited by 252 · see citing works

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future

Rony, M. K. K., Parvin, M. R., & Ferdousi, S. (2024) · Nursing Open · doi:10.1002/nop2.2070 · Cited by 202 · see citing works

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

Sahu, M., Gupta, R. V., Ambasta, R. K., & Kumar, P. (2022) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2022.03.002 · Cited by 202 · see citing works

Artificial intelligence in elderly healthcare: A scoping review

Ma, B., Yang, J. H., Wong, F. K. Y., Wong, A. K. C., Ma, T., Meng, J., Zhao, Y., Wang, Y., & Lu, Q. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101808 · Cited by 197 · see citing works

Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation

Kumar, P., Sharma, S. K., & Dutot, V. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102598 · Cited by 176 · see citing works

Design for experience – a public service design approach in the age of digitalization

Trischler, J., & Trischler, J. W. (2021) · Public Management Review · doi:10.1080/14719037.2021.1899272 · Cited by 203 · see citing works

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin, O., Yemane, A., Kembabazi, B., Malanda, C., Mwape, M. C., Mpofu, N. S., & Tigalana, D. (2020) · Journal of Engineering · doi:10.1155/2020/8090521 · Cited by 177 · see citing works

Digital Twins for Managing Health Care Systems: Rapid Literature Review

Elkefi, S., & Asan, O. (2022) · Journal of Medical Internet Research · doi:10.2196/37641 · Cited by 169 · see citing works

Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human

Mathew, D. E., Ebem, D. U., Ikegwu, A. C., Ukeoma, P. E., & Dibiaezue, N. F. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11732-2 · Cited by 168 · see citing works

Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes

Mazumdar, H., Khondakar, K. R., Das, S., Halder, A., & Kaushik, A. K. (2024) · Expert Opinion on Drug Delivery · doi:10.1080/17425247.2024.2440618 · Cited by 176 · 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

Artificial intelligence in gastroenterology: A state-of-the-art review

Kröner, P. T., Engels, M. M. L., Glicksberg, B. S., Johnson, K. W., Mzaik, O., van Hooft, J. E., Wallace, M. B., El‐Serag, H. B., & Krittanawong, C. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i40.6794 · Cited by 172 · see citing works

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Roy, S. S., Meena, T., & Lim, S. (2022) · Diagnostics · doi:10.3390/diagnostics12102549 · Cited by 165 · see citing works

Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management

Stanfill, M. H., & Marc, D. T. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677913 · Cited by 170 · see citing works

Artificial Intelligence in Hospitality and Tourism: Insights From Industry Practices, Research Literature, and Expert Opinions

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Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review

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Multirole of the internet of medical things (IoMT) in biomedical systems for managing smart healthcare systems: An overview of current and future innovative trends

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Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective

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Imputation of missing values for electronic health record laboratory data

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Artificial Intelligence in Plastic Surgery: Current Applications, Future Directions, and Ethical Implications

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Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions – A Narrative Review for a Comprehensive Insight

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Machine Learning and Surgical Outcomes Prediction: A Systematic Review

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Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics

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Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

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Artificial Intelligence in Drug Discovery and Development

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First-year nursing students’ attitudes towards artificial intelligence: Cross-sectional multi-center study

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Revolutionizing Drug Discovery: A Comprehensive Review of AI Applications

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Exploring benefits and ethical challenges in the rise of mHealth (mobile healthcare) technology for the common good: An analysis of mobile applications for health specialists

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Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity

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Applications of artificial intelligence and machine learning in heart failure

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Investigation and Assessment of AI’s Role in Nutrition—An Updated Narrative Review of the Evidence

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Novel binary logistic regression model based on feature transformation of XGBoost for type 2 Diabetes Mellitus prediction in healthcare systems

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Equity within AI systems: What can health leaders expect?

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Next-generation business models for artificial intelligence start-ups in the healthcare industry

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An efficient image encryption scheme for healthcare applications

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The role of regional analgesia in personalized postoperative pain management

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The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

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Artificial intelligence in pharmacology research and practice

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Artificial Intelligence in Medicine and Dentistry

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Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals

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The Application of Artificial Intelligence in the Genetic Study of Alzheimer’s Disease

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Prediction of Long-Term Stroke Recurrence Using Machine Learning Models

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Artificial Intelligence in Drug Discovery and Development

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The role of explainability and transparency in fostering trust in AI healthcare systems: a systematic literature review, open issues and potential solutions

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Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review

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Integrating large language models in systematic reviews: a framework and case study using ROBINS-I for risk of bias assessment

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What Makes Artificial Intelligence Exceptional in Health Technology Assessment?

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Strategies to improve the impact of artificial intelligence on health equity: scoping review

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Best practices in the real-world data life cycle

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The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review

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Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

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Nursing students' attitudes and literacy toward artificial intelligence: a cross-sectional study

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Exploring the current and prospective role of artificial intelligence in disease diagnosis

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Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review

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Student perspectives on the integration of artificial intelligence into healthcare services

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Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis

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Architecture of Smart Health Care System Using Artificial Intelligence

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Artificial intelligence (AI)-augmented knowledge management capability and clinical performance: implications for marketing strategies in health-care sector

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Debunking the myth of industry 4.0 in health care: insights from a systematic literature review

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Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop

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Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study

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Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event

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Public patient views of artificial intelligence in healthcare: A nominal group technique study

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The unmet potential of artificial intelligence in veterinary medicine

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Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis

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Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

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Simulation and Artificial Intelligence in Rhinoplasty: A Systematic Review

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Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

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American public opinion on artificial intelligence in healthcare

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Artificial intelligence enabled applications in kidney disease

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Artificial Intelligence in Reproductive Medicine – An Ethical Perspective

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Qualitative evaluation of artificial intelligence-generated weight management diet plans

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Innovation and application of Large Language Models (LLMs) in dentistry – a scoping review

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Early Detection of Septic Shock Onset Using Interpretable Machine Learners

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Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future

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Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework

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Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review

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Precision Medicine Informatics: Principles, Prospects, and Challenges

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Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review

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AI and Blockchain for Healthcare Data Security in Smart Cities

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AI in Mental Health: A Review of Technological Advancements and Ethical Issues in Psychiatry

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Readiness Towards Artificial Intelligence Among Undergraduate Medical Students in Malaysia

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Trust Me, I’m a Doctor – User Perceptions of AI-Driven Apps for Mobile Health Diagnosis

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Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence

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Predicting short and long-term mortality after acute ischemic stroke using EHR

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Artificial intelligence and the future of life sciences

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Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

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Artificial Intelligence and Healthcare Ethics

Rana, A., Goedmakers, C. M. W., & Smith, T. R. (2021) · Springer eBooks · doi:10.1007/978-3-030-78075-3_31

Practical guidance on artificial intelligence for health-care data

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The Reply

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Inteligencia artificial para el avance de los sistemas de salud. Posibles aportes y retos

Bermúdez‐Tamayo, C., & Jiménez-Pernett, J. (2022) · Revista de Derecho de la Seguridad Social, Laborum

Personalized, Data-Rich, and Population-Level Care

Owais Durrani., M. D., & Glatter, R. (2026) · CRC Press eBooks · doi:10.1201/9781003767244-9