Lost in thought—the limits of the human mind and the future of medicine

Identification of type 2 diabetes subgroups through topological analysis of patient similarity

Li, L., Cheng, W., Glicksberg, B. S., Gottesman, O., Tamler, R., Chen, R., Böttinger, E. P., & Dudley, J. T. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa9364 · Cited by 548 · see citing works

Does Machine Learning Automate Moral Hazard and Error?

Mullainathan, S., & Obermeyer, Z. (2017) · American Economic Review · doi:10.1257/aer.p20171084 · Cited by 156 · see citing works

Early death after discharge from emergency departments: analysis of national US insurance claims data

Obermeyer, Z., Cohn, B., Wilson, M., Jena, A. B., & Cutler, D. (2017) · BMJ · doi:10.1136/bmj.j239 · Cited by 103 · see citing works

Computationally Generated Cardiac Biomarkers for Risk Stratification After Acute Coronary Syndrome

Syed, Z., Stultz, C. M., Scirica, B. M., & Guttag, J. V. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002557 · Cited by 31 · see citing works

Machine Learning in Medicine

Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579 · see citing works

Artificial intelligence in retina

Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.07.004 · Cited by 779 · see citing works

The Society of Algorithms

Burrell, J., & Fourcade, M. (2021) · Annual Review of Sociology · doi:10.1146/annurev-soc-090820-020800 · Cited by 491 · see citing works

Patient apprehensions about the use of artificial intelligence in healthcare

Richardson, J. P., Smith, C., Curtis, S., Watson, S., Zhu, X., Barry, B., & Sharp, R. R. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00509-1 · Cited by 424 · see citing works

Machine learning for real-time prediction of complications in critical care: a retrospective study

Meyer, A., Zverinski, D., Pfahringer, B., Kempfert, J., Kuehne, T., Sündermann, S. H., Stamm, C., Hofmann, T., Falk, V., & Eickhoff, C. (2018) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(18)30300-x · Cited by 350 · see citing works

Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners’ Views

Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019) · Journal of Medical Internet Research · doi:10.2196/12802 · Cited by 346 · see citing works

Machine Learning and the Future of Cardiovascular Care

Quer, G., Arnaout, R., Henne, M., & Arnaout, R. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.11.030 · Cited by 346 · see citing works

Accessing Artificial Intelligence for Clinical Decision-Making

Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326 · see citing works

A Review of Challenges and Opportunities in Machine Learning for Health

Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2018) · PubMed Central · doi:10.48550/arxiv.1806.00388 · Cited by 347 · see citing works

Machine learning and medical education

Kolachalama, V. B., & Garg, P. S. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0061-1 · Cited by 320 · see citing works

Big Data and Data Science in Critical Care

Sanchez-Pinto, L. N., Luo, Y., & Churpek, M. M. (2018) · CHEST Journal · doi:10.1016/j.chest.2018.04.037 · Cited by 284 · see citing works

Reimagining Medical Education in the Age of AI

(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.146 · Cited by 263 · see citing works

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Thompson, R. F., Valdés, G., Fuller, C. D., Carpenter, C. M., Morin, O., Aneja, S., Lindsay, W. D., Aerts, H. J. W. L., Agrimson, B., Deville, C., et al. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.05.030 · Cited by 260 · see citing works

Artificial intelligence and the future of psychiatry: Insights from a global physician survey

Doraiswamy, P. M., Blease, C., & Bodner, K. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101753 · Cited by 253 · see citing works

Machine Learning in Orthopedics: A Literature Review

Cabitza, F., Locoro, A., & Banfi, G. (2018) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2018.00075 · Cited by 228 · see citing works

Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer

Bibault, J., Giraud, P., Housset, M., Durdux, C., Taı̈eb, J., Berger, A., Coriat, R., Chaussade, S., Dousset, B. E., Nordlinger, B., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-30657-6 · Cited by 229 · see citing works

Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts

Forrest, I. S., Petrazzini, B. O., Duffy, Á., Park, J. K., Márquez‐Luna, C., Jordan, D. M., Rocheleau, G., Cho, J. H., Rosenson, R. S., Narula, J., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)02079-7 · Cited by 179 · see citing works

A Path for Translation of Machine Learning Products into Healthcare Delivery

(2020) · EMJ Innovations · doi:10.33590/emjinnov/19-00172 · Cited by 172 · see citing works

Humans inherit artificial intelligence biases

Vicente, L., & Matute, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-42384-8 · Cited by 158 · see citing works

Application of machine learning in ophthalmic imaging modalities

Tong, Y., Lü, W., Yu, Y., & Shen, Y. (2020) · Eye and Vision · doi:10.1186/s40662-020-00183-6 · Cited by 152 · see citing works

AI-induced deskilling in medicine: a mixed-method review and research agenda for healthcare and beyond

Natali, C., Marconi, L., Dias Duran, L. D., & Cabitza, F. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11352-1 · Cited by 143 · see citing works

Predicting with Proxies: Transfer Learning in High Dimension

Bastani, H. (2020) · Management Science · doi:10.1287/mnsc.2020.3729 · Cited by 140 · see citing works

An Introduction to Machine Learning for Clinicians

Rowe, M. (2019) · Academic Medicine · doi:10.1097/acm.0000000000002792 · Cited by 138 · see citing works

Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey

Blease, C., Locher, C., Leon-Carlyle, M., & Doraiswamy, M. (2020) · DIGITAL HEALTH · doi:10.1177/2055207620968355 · Cited by 121 · see citing works

Use of Steroid Profiling Combined With Machine Learning for Identification and Subtype Classification in Primary Aldosteronism

Eisenhofer, G., Durán, C., Cannistraci, C. V., Peitzsch, M., Williams, T. A., Riester, A., Burrello, J., Buffolo, F., Prejbisz, A., Beuschlein, F., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.16209 · Cited by 116 · see citing works

Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma

Yun, J., Park, J. E., Lee, H., Ham, S., Kim, N., & Kim, H. S. (2019) · Scientific Reports · doi:10.1038/s41598-019-42276-w · Cited by 113 · see citing works

Three Problems with Big Data and Artificial Intelligence in Medicine

Chin-Yee, B. H., & Upshur, R. E. G. (2019) · Perspectives in biology and medicine · doi:10.1353/pbm.2019.0012 · Cited by 113 · see citing works

Applying machine learning to continuously monitored physiological data

Rush, B. N. M., Celi, L. A., & Stone, D. J. (2018) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-018-0219-z · Cited by 93 · see citing works

Computerization and the future of primary care: A survey of general practitioners in the UK

Blease, C. R., Bernstein, M. H., Gaab, J., Kaptchuk, T. J., Kossowsky, J., Mandl, K. D., Davis, R. B., & DesRoches, C. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207418 · Cited by 92 · see citing works

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 91 · see citing works

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK

McWilliams, C., Lawson, D. J., Santos‐Rodríguez, R., Gilchrist, I. D., CHAMPNEYS, A. R., Gould, T. H., Thomas, M. J., & Bourdeaux, C. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025925 · Cited by 92 · see citing works

An eHealth Capabilities Framework for Graduates and Health Professionals: Mixed-Methods Study

Brunner, M., McGregor, D., Keep, M., Janssen, A. L., Spallek, H., Quinn, D., Jones, A., Tseris, E. J., Yeung, W., Togher, L., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/10229 · Cited by 87 · see citing works

Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study

Ting, D. S. W., Cheung, C. Y.-L., Nguyen, Q., Sabanayagam, C., Lim, G., Lim, Z. W., Tan, G. S. W., Soh, Y. Q., Schmetterer, L. F., Wang, Y. X., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0097-x · Cited by 82 · see citing works

Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland

Blease, C., Kharko, A., Bernstein, M., Bradley, C., Houston, M., Walsh, I., Hägglund, M., DesRoches, C., & Mandl, K. D. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100480 · Cited by 81 · see citing works

When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis

Ahsen, M. E., Ayvaci, M., & Raghunathan, S. (2017) · Information Systems Research · doi:10.1287/isre.2018.0789 · Cited by 78 · see citing works

Accuracy of a machine learning muscle MRI-based tool for the diagnosis of muscular dystrophies

Verdú-Díaz, J., Alonso‐Pérez, J., Nuñez‐Peralta, C., Tasca, G. A., Vissing, J., Straub, V. W., Fernández‐Torrón, R., Llauger, J., Illa, I., & Díaz‐Manera, J. (2020) · Neurology · doi:10.1212/wnl.0000000000009068 · Cited by 78 · see citing works

Medical-informed machine learning: integrating prior knowledge into medical decision systems

Sirocchi, C., Bogliolo, A., & Montagna, S. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02582-4 · Cited by 81 · see citing works

Bringing complexity into clinical practice: An internistic approach

Corazza, G. R., Formagnana, P., & Lenti, M. V. (2018) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2018.11.009 · Cited by 74 · see citing works

The proof of the pudding: in praise of a culture of real-world validation for medical artificial intelligence

Cabitza, F., & Zeitoun, J.-D. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.04.07 · Cited by 72 · see citing works

Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort

Pamporaki, C., Berends, A. M. A., Filippatos, A., Prodanov, T., Meuter, L., Prejbisz, A., Beuschlein, F., Faßnacht, M., Timmers, H. J. L. M., Nölting, S., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00094-8 · Cited by 70 · see citing works

The impact of artificial intelligence on remote healthcare: Enhancing patient engagement, connectivity, and overcoming challenges

Chaturvedi, U., Chauhan, S. B., & Singh, I. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.12.003 · Cited by 96 · see citing works

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon, C., Kaewput, W., Kovvuru, K. R., Hansrivijit, P., Kanduri, S. R., Bathini, T. J., Chewcharat, A., Leeaphorn, N., Suarez, M. L. G., & Cheungpasitporn, W. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9041107 · Cited by 67 · see citing works

Machine learning and acute stroke imaging

Sheth, S. A., Giancardo, L., Colasurdo, M., Srinivasan, V. M., Niktabe, A., & Kan, P. T. M. (2022) · Journal of NeuroInterventional Surgery · doi:10.1136/neurintsurg-2021-018142 · Cited by 62 · see citing works

NCTN Assessment on Current Applications of Radiomics in Oncology

Nie, K., Al‐Hallaq, H., Li, X. A., Benedict, S., Sohn, J. W., Moran, J. M., Yong Fan, A., Huang, M., Knopp, M. V., Michalski, J. M., et al. (2019) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2019.01.087 · Cited by 63 · see citing works

Data Resource Profile: Regional healthcare information platform in Halland, Sweden

Ashfaq, A., Lönn, S., Nilsson, H., Eriksson, J. A., Kwatra, J., Yasin, Z. M., Slutzman, J. E., Wallenfeldt, T., Obermeyer, Z., Anderson, P. D., et al. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz262 · Cited by 61 · see citing works

Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis

Ahmed, Z. (2022) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2022.02.002 · Cited by 57 · see citing works

Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Seccia, R., Romano, S., Salvetti, M., Crisanti, A., Palagi, L., & Grassi, F. (2021) · Life · doi:10.3390/life11020122 · Cited by 56 · see citing works

Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study

Lee, K.-S., Jang, J.-Y., Yu, Y.-D., Heo, J. S., Han, H.-S., Yoon, Y.-S., Kang, C. M., Hwang, H. K., & Kang, S. (2021) · International Journal of Surgery · doi:10.1016/j.ijsu.2021.106050 · Cited by 55 · see citing works

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Seccia, R., Gammelli, D., Dominici, F., Romano, S., Landi, A. C., Salvetti, M., Tacchella, A., Zaccaria, A., Crisanti, A., Grassi, F., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230219 · Cited by 55 · see citing works

Rheumatology 4.0: big data, wearables and diagnosis by computer

Burmester, G. R. (2018) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2017-212888 · Cited by 53 · see citing works

Critical Care, Critical Data

Cosgriff, C. V., Celi, L. A., & Stone, D. J. (2019) · Biomedical Engineering and Computational Biology · doi:10.1177/1179597219856564 · Cited by 49 · see citing works

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Ahmed, Z. (2022) · Emerging Topics in Life Sciences · doi:10.1042/etls20210244 · Cited by 47 · see citing works

Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer

Dong, T., Yang, C., Cui, B., Zhang, T., Sun, X., Song, K., Wang, L., Kong, B., & Yang, X. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00464 · Cited by 46 · see citing works

Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov

Dong, J., Geng, Y., Lu, D., Li, B., Tian, L., Lin, D. C.-C., & Zhang, Y. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01629 · Cited by 47 · see citing works

Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease

Piccialli, F., Calabrò, F., Crisci, D., Cuomo, S., Prezioso, E., Mandile, R., Troncone, R., Greco, L., & Auricchio, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-84951-x · Cited by 47 · see citing works

Digital Futures Past — The Long Arc of Big Data in Medicine

Greene, J. A., & Lea, A. S. (2019) · New England Journal of Medicine · doi:10.1056/nejmms1817674 · Cited by 45 · see citing works

The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases

Desideri, L. F., Rutigliani, C., Corazza, P., Nastasi, A., Roda, M., Nicolò, M., Traverso, C. E., & Vagge, A. (2022) · Journal of Optometry · doi:10.1016/j.optom.2022.08.001 · Cited by 44 · see citing works

Health Information Counselors: A New Profession for the Age of Big Data

Fiske, A., Buyx, A., & Prainsack, B. (2018) · Academic Medicine · doi:10.1097/acm.0000000000002395 · Cited by 44 · see citing works

Negative Patient-Experience Comments After Total Shoulder Arthroplasty

Menendez, M. E., Shaker, J. M., Lawler, S. M., Ring, D., & Jawa, A. (2019) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.18.00695 · Cited by 43 · see citing works

Artificial intelligence for predicting survival following deceased donor liver transplantation: Retrospective multi-center study

Yu, Y., Lee, K.-S., Kim, J. M., Ryu, J. H., Lee, J. G., Lee, K., Kim, B., & Kim, D. (2022) · International Journal of Surgery · doi:10.1016/j.ijsu.2022.106838 · Cited by 42 · see citing works

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists

Musacchio, N., Giancaterini, A., Guaita, G., Ozzello, A., Pellegrini, M. A., Ponzani, P., Russo, G. T., Zilich, R., & Micheli, A. D. (2020) · Journal of Medical Internet Research · doi:10.2196/16922 · Cited by 44 · see citing works

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions

Morris, A. H., Stagg, B. C., Lanspa, M. J., Orme, J. F., Clemmer, T. P., Weaver, L. K., Thomas, F., Grissom, C. K., Hirshberg, E. L., East, T. D., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa294 · Cited by 40 · see citing works

Inflammatory Bowel Disease: Complexity and Variability Need Integration

Fiocchi, C. (2018) · Frontiers in Medicine · doi:10.3389/fmed.2018.00075 · Cited by 39 · see citing works

Big data in nephrology: Are we ready for the change?

Yang, C., Kong, G., Wang, L., Zhang, L., & Zhao, M. (2019) · Nephrology · doi:10.1111/nep.13636 · Cited by 38 · see citing works

A machine learning model identifies patients in need of autoimmune disease testing using electronic health records

Forrest, I. S., Petrazzini, B. O., Duffy, Á., Park, J. K., O’Neal, A. J., Jordan, D. M., Rocheleau, G., Nadkarni, G. N., Cho, J. H., Blazer, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37996-7 · Cited by 37 · see citing works

Precision Medicine and Global Health: The Good, the Bad, and the Ugly

Mentis, A. A., Pantelidi, K., Dardiotis, E., Hadjigeorgiou, G. M., & Petinaki, E. (2018) · Frontiers in Medicine · doi:10.3389/fmed.2018.00067 · Cited by 37 · see citing works

The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions

Ermers, N. J., Hagoort, K., & Scheepers, F. E. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00472 · Cited by 39 · see citing works

Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data

Schenck, E. J., Hoffman, K. L., Cusick, M. M., Kabariti, J., Sholle, E., & Campion, T. R. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103789 · Cited by 36 · see citing works

A Framework for Interpretability in Machine Learning for Medical Imaging

Wang, A. Q., Karaman, B. K., Kim, H., Rosenthal, J., Saluja, R., Young, S. I., & Sabuncu, M. R. (2024) · IEEE Access · doi:10.1109/access.2024.3387702 · Cited by 37 · see citing works

Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department

Ouchi, K., Lindvall, C. J., Chai, P. R., & Boyer, E. W. (2018) · Journal of Medical Toxicology · doi:10.1007/s13181-018-0667-3 · Cited by 34 · see citing works

Artificial intelligence and the hunt for immunological disorders

Rider, N. L., Srinivasan, R., & Khoury, P. (2020) · Current Opinion in Allergy and Clinical Immunology · doi:10.1097/aci.0000000000000691 · Cited by 32 · see citing works

The application of artificial intelligence in hepatology: A systematic review

Balsano, C., Alisi, A., Brunetto, M. R., Invernizzi, P., Burra, P., Piscaglia, F., Alvaro, D., Bonino, F., Carbone, M., Faita, F., et al. (2021) · Digestive and Liver Disease · doi:10.1016/j.dld.2021.06.011 · Cited by 32 · see citing works

Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning

González‐Nóvoa, J. A., Campanioni, S., Busto, L., Fariña, J., Rodriguez-Andina, J. J., Vila, D., Íñiguez, A., & Veiga, C. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043455 · Cited by 30 · see citing works

Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

Hoque, M. E., & Kipli, K. (2021) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v17i14.24819 · Cited by 31 · see citing works

The Transformation of Healthcare Through AI-Driven Diagnostics

Tariq, M. U. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-5261-8.ch015 · Cited by 29 · see citing works

Anticipating artificial intelligence in mammography screening: views of Swedish breast radiologists

Högberg, C., Larsson, S., & Lång, K. (2023) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2022-100712 · Cited by 28 · see citing works

How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease

Silverman, A. L., Shung, D. L., Stidham, R. W., Kochhar, G. S., & Iacucci, M. (2024) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2024.05.048 · Cited by 30 · see citing works

Impact of big data on oral health outcomes

Nanayakkara, S., Zhou, X., & Spallek, H. (2018) · Oral Diseases · doi:10.1111/odi.13007 · Cited by 28 · see citing works

Machine learning–based penetrance of genetic variants

Forrest, I. S., Vy, H. M. T., Rocheleau, G., Jordan, D. M., Petrazzini, B. O., Nadkarni, G. N., Cho, J. H., Ganapathi, M., Huang, K., Chung, W. K., et al. (2025) · Science · doi:10.1126/science.adm7066 · Cited by 27 · see citing works

COVID-19 and digital competencies among young physicians: are we (really) ready for the new era? A national survey of the Italian Young Medical Doctors Association.

Casà, C., Marotta, C., Pumpo, M. D., Cozzolino, A., D'Aviero, A., Frisicale, E. M., Silenzi, A., Gabbrielli, F., Bertinato, L., & Brusaferro, S. (2021) · Annali dell Istituto Superiore di Sanità · doi:10.4415/ann_21_01_01 · Cited by 26 · see citing works

Attitudes of Anesthesiologists toward Artificial Intelligence in Anesthesia: A Multicenter, Mixed Qualitative–Quantitative Study

Henckert, D., Malorgio, A., Schweiger, G., Raimann, F. J., Piekarski, F., Zacharowski, K. D., Hottenrott, S., Meybohm, P., Tscholl, D. W., Spahn, D. R., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12062096 · Cited by 26 · see citing works

Natural Language Processing of Patient-Experience Comments After Primary Total Knee Arthroplasty

Bovonratwet, P., Shen, T. S., Islam, W., Ast, M. P., Haas, S. B., & Su, E. P. (2020) · The Journal of Arthroplasty · doi:10.1016/j.arth.2020.09.055 · Cited by 26 · see citing works

Precision Medicine: Academic dreaming or clinical reality?

Josephson, C. B., & Wiebe, S. (2020) · Epilepsia · doi:10.1111/epi.16739 · Cited by 26 · see citing works

Application of Artificial Intelligence in Targeting Retinal Diseases

Sorrentino, F. S., Jurman, G., Nadai, K. D., Campa, C., Furlanello, C., & Parmeggiani, F. (2020) · Current Drug Targets · doi:10.2174/1389450121666200708120646 · Cited by 25 · see citing works

Inteligencia artificial y aprendizaje automático en medicina

Vega, M. Á., Mora, L. M. Q., & Badilla, M. V. C. (2020) · Revista Medica Sinergia · doi:10.31434/rms.v5i8.557 · Cited by 32 · see citing works

The Environmental Costs of Artificial Intelligence for Healthcare

Katirai, A. (2024) · Asian Bioethics Review · doi:10.1007/s41649-024-00295-4 · Cited by 27 · see citing works

Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study

Blom, M., Ashfaq, A., Sant’Anna, A., Anderson, P. D., & Lingman, M. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-028015 · Cited by 21 · see citing works

Network Medicine: A Mandatory Next Step for Inflammatory Bowel Disease

de Souza, H. S. P., & Fiocchi, C. (2018) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izx111 · Cited by 20 · see citing works

Personalized Pancreatic Cancer Management

Bradley, A., Meer, R. V. D., & McKay, C. J. (2019) · Pancreas · doi:10.1097/mpa.0000000000001312 · Cited by 20 · see citing works

IBD Systems Biology Is Here to Stay

Fiocchi, C., & Iliopoulos, D. (2021) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa343 · Cited by 20 · see citing works

Computerized history-taking improves data quality for clinical decision-making—Comparison of EHR and computer-acquired history data in patients with chest pain

Zakim, D., Brandberg, H., Amrani, S. E., Hultgren, A., Stathakarou, N., Nifakos, S., Kahan, T., Spaak, J., Koch, S., & Sundberg, C. J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257677 · Cited by 20 · see citing works

Integration of Machine Learning and Pharmacogenomic Biomarkers for Predicting Response to Antidepressant Treatment: Can Computational Intelligence be used to Augment Clinical Assessments?

Athreya, A. P., Iyer, R. K., Wang, L., Weinshilboum, R. M., & Bobo, W. V. (2019) · Pharmacogenomics · doi:10.2217/pgs-2019-0119 · Cited by 21 · see citing works

Development and Trends in Artificial Intelligence in Critical Care Medicine: A Bibliometric Analysis of Related Research over the Period of 2010–2021

Cui, X., Chang, Y., Yang, C., Cong, Z., Wang, B., & Leng, Y. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010050 · Cited by 19 · see citing works

A Surgeon’s Guide to Machine Learning

Lammers, D. T., Eckert, C., Ahmad, M. A., Bingham, J. R., & Eckert, M. J. (2021) · Annals of Surgery Open · doi:10.1097/as9.0000000000000091 · Cited by 19 · see citing works

Risks and benefits of dermatological machine learning health care applications—an overview and ethical analysis

Willem, T., Krammer, S., Böhm, A. ‐., French, L. E., Hartmann, D., Lasser, T., & Buyx, A. (2022) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18192 · Cited by 18 · see citing works

Machine Learning for Renal Pathologies: An Updated Survey

Magherini, R., Mussi, E., Volpe, Y., Furferi, R., Buonamici, F., & Servi, M. (2022) · Sensors · doi:10.3390/s22134989 · Cited by 19 · see citing works

Saving Lives With Algorithm-Enabled Process Innovation for Sepsis Care

Adjerid, I., Ayvaci, M., & Özer, Ö. (2019) · Manufacturing & Service Operations Management · doi:10.1287/msom.2023.1226 · Cited by 19 · see citing works

Deep learning enables automated localization of the metastatic lymph node for thyroid cancer on 131I post-ablation whole-body planar scans

Kavitha, M. S., Lee, C.-H., Shibudas, K., Kurita, T., & Ahn, B. (2020) · Scientific Reports · doi:10.1038/s41598-020-64455-w · Cited by 19 · see citing works

The challenge of implementing AI models in the ICU

Norrie, J. D. (2018) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(18)30412-0 · Cited by 19 · see citing works

AI implementation in pediatric radiology for patient safety: a multi-society statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN

Shelmerdine, S. C., Naidoo, J., Kelly, B. S., Laborie, L. B., Toso, S., D’Antonoli, T. A., Arthurs, O. J., Blumer, S. L., Ciet, P., Damasio, M. B., et al. (2025) · Pediatric Radiology · doi:10.1007/s00247-025-06386-0 · Cited by 18 · see citing works

Can computers replace medical thinking?

Buta, M. G. (2023) · Medicine and Pharmacy Reports · doi:10.15386/mpr-2523

Hearing without Listening

Drazen, J. M. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1612158 · Cited by 3 · see citing works

What About the Future?

Schwichtenberg, A. H. (1965) · JAMA · doi:10.1001/jama.1965.03080110052013 · Cited by 7 · see citing works

Shedding our illusions: a better way of medicine

Weed, L. L. (2004) · Fertility and Sterility · doi:10.1016/s0015-0282(03)01143-9 · Cited by 8 · see citing works

COMPUTERS AND THE PRACTICE OF MEDICINE

(1961) · JAMA · doi:10.1001/jama.1961.03040290033010