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 554

Does Machine Learning Automate Moral Hazard and Error?

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

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

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

The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?

Auconi, P., Gili, T., Capuani, S., Saccucci, M., Caldarelli, G., Polimeni, A., & Carlo, G. D. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12060957 · Cited by 17

Advancements in predicting outcomes in patients with glioma: a surgical perspective

Jakola, A. S., Sagberg, L. M., Gulati, S., & Solheim, O. S. (2020) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2020.1735367 · Cited by 18

DigitalMe: a journey towards personalized health and thriving

Okun, S., & Wicks, P. J. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0553-x · Cited by 17

From Big Data to Artificial Intelligence: Harnessing Data Routinely Collected in the Process of Care*

Rush, B. N. M., Stone, D. J., & Celi, L. A. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000002892 · Cited by 18

Complexity and data mining in dental research: A network medicine perspective on interceptive orthodontics

Gili, T., Carlo, G. D., Capuani, S., Auconi, P., Caldarelli, G., & Polimeni, A. (2021) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.12520 · Cited by 18

Artificial intelligence in pharmacy: A guide for clinicians

Smoke, S. M. (2024) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxae051 · Cited by 16

A prognostic Bayesian network that makes personalized predictions of poor prognostic outcome post resection of pancreatic ductal adenocarcinoma

Bradley, A., Meer, R. V. D., & McKay, C. J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222270 · Cited by 16

Engaging with artificial intelligence in mammography screening: Swedish breast radiologists’ views on trust, information and expertise

Högberg, C., Larsson, S., & Lång, K. (2024) · Digital Health · doi:10.1177/20552076241287958 · Cited by 18

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning

Xiao, H., Liang, X., Li, H., Chen, X., & Li, Y. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.117238 · Cited by 15

Artificial intelligence with kidney disease

Park, S., Park, B. S., Lee, Y. J., Kim, I. H., Park, J. H., Ko, J., Kim, Y. W., & Park, K. M. (2021) · Medicine · doi:10.1097/md.0000000000025422 · Cited by 15

Difficult diagnosis in the ICU : making the right call but beware uncertainty and bias

Pisciotta, W., Arina, P., Hofmaenner, D. A., & Singer, M. (2023) · Anaesthesia · doi:10.1111/anae.15897 · Cited by 16

Telemonitoring in patients with chronic heart failure

Mareev, Y. V., Zinchenko, A., Myasnikov, R. P., Vakhovskaya, T. V., Andreenko, Е. Y., Boytsov, S. A., & Drapkina, O. M. (2019) · Kardiologiia · doi:10.18087/cardio.n530 · Cited by 14

Digital Health Primer for Cardiothoracic Surgeons

Baxter, R. D., Fann, J. I., DiMaio, J. M., & Lobdell, K. W. (2020) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2020.02.072 · Cited by 14

National retrospective cohort study to identify age-specific fatality risks of comorbidities among hospitalised patients with influenza-like illness in Taiwan

Weng, T.-C., Chiu, H.-Y. R., Chen, S., Shih, F., King, C., & Fang, C. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025276 · Cited by 14

Coronary plaque phenotype associated with positive remodeling

Kinoshita, D., Suzuki, K., Yuki, H., Niida, T., Fujimoto, D., Minami, Y., Dey, D., Lee, H., McNulty, I. A., Ako, J., et al. (2024) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2024.04.009 · Cited by 13

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients

Cowley, H., Robinette, M. S., Matelsky, J., Xenes, D., Kashyap, A., Ibrahim, N. F., Robinson, M. L., Zeger, S. L., Garibaldi, B. T., & Gray-Roncal, W. (2023) · Scientific Reports · doi:10.1038/s41598-022-26294-9 · Cited by 12

Evaluating Large Language Models on American Board of Anesthesiology-style Anesthesiology Questions: Accuracy, Domain Consistency, and Clinical Implications

Patel, S., Ngo, V., & Wilhelmi, B. G. (2025) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2025.05.033 · Cited by 11

Artificial intelligence applied to ophthalmology and optometry: A citation network analysis

Martínez-Pérez, C., Alvarez-Peregrina, C. C., Villa-Collar, C. C., & Sánchez‐Tena, M. Á. (2022) · Journal of Optometry · doi:10.1016/j.optom.2022.06.005 · Cited by 13

A Rapid Detection of Parkinson’s Disease using Smart Insoles: A Statistical and Machine Learning Approach

D’Arco, L., Wang, H., & Zheng, H. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995237 · Cited by 11

Evidence-Based Medicine: Past, Present, Future

Triposkiadis, F. K., & Brutsaert, D. L. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14145094 · Cited by 25

Priorities to Overcome Barriers Impacting Data Science Application in Emergency Care Research

Puskarich, M. A., Callaway, C. W., Silbergleit, R., Pines, J. M., Obermeyer, Z., Wright, D. W., Hsia, R. Y.-J., Shah, M. N., Monte, A. A., Limkakeng, A. T., et al. (2018) · Academic Emergency Medicine · doi:10.1111/acem.13520 · Cited by 10

AI-Induced Deskilling in Medicine: A Mixed Method Literature Review for Setting a New Research AgendaPreprint

Natali, C., Marconi, L., Duran, L. D. D., Miglioretti, M., & Cabitza, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5166364 · Cited by 10

Future proofing expertise: AI as a power-up, not a takeover

Toscani, G. (2025) · Futures · doi:10.1016/j.futures.2025.103728 · Cited by 9

Applications and Effects of EdTech in Medical Education

Hong, H., & Kim, Y. (2021) · Korean Medical Education Review · doi:10.17496/kmer.2021.23.3.160 · Cited by 9

Developer Perspectives on Potential Harms of Machine Learning Predictive Analytics in Health Care: Qualitative Analysis

Nichol, A., Sankar, P. L., Halley, M. C., Federico, C. A., & Cho, M. K. (2023) · Journal of Medical Internet Research · doi:10.2196/47609 · Cited by 9

Overachieving Municipalities in Public Health: A Machine-learning Approach

Filho, A. D. P. C., dos Santos, H. G., do Nascimento, C. F., Massa, K. H. C., & Kawachi, I. I. (2018) · Epidemiology · doi:10.1097/ede.0000000000000919 · Cited by 9

Raising the stakeholders: Improving patient outcomes through interprofessional collaborations in AI for healthcare

Bobak, C. A., Svoboda, M., Giffin, K. A., Wall, D. P., & Moore, J. H. (2020) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811232701_0035 · Cited by 10

Evaluating Teaching Effectiveness of Medical Humanities in an Integrated Clerkship Program by a Novel Prospective Propensity Score Matching Framework

Chen, C., Wang, S., Yeh, W., Wu, C., Wang, Y. A., Chen, C.-F., Yang, Y., Huang, W. J., Chan, K.-Y., Lai, C., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031882 · Cited by 9

Effective resource management using machine learning in medicine: an applied example

Williams, A., Mekhail, A.-M., Williams, J. B., McCord, J., & Buchan, V. (2018) · BMJ Simulation & Technology Enhanced Learning · doi:10.1136/bmjstel-2017-000289 · Cited by 8

Management of ST-elevation myocardial infarction in the setting of anterior epistaxis: focused on antiplatelet and antithrombotic therapies

Handoyo, V., Pertiwi, G. A. R., Prabawa, I. P. Y., Manuaba, I. B. P., Bhargah, A., & Budiana, I. P. G. (2019) · International Medical Case Reports Journal · doi:10.2147/imcrj.s189370 · Cited by 8

Dual Stage Normalization Approach Towards Classification of Breast Cancer

A., A. M., & Jagannath, M. (2020) · IETE Journal of Research · doi:10.1080/03772063.2020.1754140 · Cited by 9

A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback

Nawaz, A., Abbas, Y., Ahmad, T., Mahmoud, N. F., Rizwan, A., & Samee, N. M. A. (2022) · Healthcare · doi:10.3390/healthcare10081592 · Cited by 8

Gaussian Process-Based Models for Clinical Time Series in Healthcare

Futoma, J. (2018) · Cited by 8

Early prediction of hypothermia in pediatric intensive care units using machine learning

Singh, P., Nagori, A., Lodha, R., & Sethi, T. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.921884 · Cited by 8

Differentiation of glioblastoma from solitary brain metastasis using deep ensembles: Empirical estimation of uncertainty for clinical reliability

Park, Y. W., Eom, S., Kim, S., Lim, S., Park, J. E., Kim, H. S., You, S. C., Ahn, S. S., & Lee, S. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108288 · Cited by 9

A predictive Bayesian network that risk stratifies patients undergoing Barrett’s surveillance for personalized risk of developing malignancy

Bradley, A., Sami, S., Hwei, N. G., Macleod, A., Prasanth, M. N., Zafar, M., Hemadasa, N., Neagle, G., Rosindell, I., & Apollos, J. R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240620 · Cited by 7

Artificial Intelligence in Medical Education: A citation-based systematic literature review

Burney, I. A., & Ahmad, N. (2022) · Journal of Shifa Tameer-e-Millat University · doi:10.32593/jstmu/vol5.iss1.183 · Cited by 13

Prioritizing educational initiatives on emerging technologies for Italian pediatricians: bibliometric review and a survey

Tozzi, A. E., Gesualdo, F., Pandolfi, E., Ferro, D., Cinelli, G., Bozzola, E., Aversa, T., Mauro, A. D., Mameli, C., & Croci, I. (2023) · The Italian Journal of Pediatrics/Italian journal of pediatrics · doi:10.1186/s13052-023-01512-w · Cited by 8

A Reflection Upon the Impact of Early 21st-Century Technological Innovations on Medical School Admissions

Hanson, M. D., & Eva, K. W. (2019) · Academic Medicine · doi:10.1097/acm.0000000000002590 · Cited by 7

Biomedical Data Annotation: An OCT Imaging Case Study

Anderson, M., Sadiq, S. N., Solim, M. A. N., Barker, H., STEEL, D. H., Habib, M., & Obara, B. (2023) · Journal of Ophthalmology · doi:10.1155/2023/5747010 · Cited by 6

Artificial Intelligence Implementation in Pediatric Radiology for Patient Safety: A Multisociety 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) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2025.08.019 · Cited by 6

PainAPPle®. Validation and evaluation of an electronic application for the management of acute pain in pediatric patients

García, E. M., Escudero, P. C., Mateos-Arroyo, J. A., Ramos-Luengo, A., Alonso, F. S., & Barbero, F. R. (2020) · Revista Española de Anestesiología y Reanimación (English Edition) · doi:10.1016/j.redare.2019.09.007 · Cited by 5

Internet of Medical Things (IoMT) & Secured Using Steganography for Development of Smart Society 5.0

Dhawan, S., Gupta, R., Rana, A. K., & Sharma, S. C. (2022) · Asset analytics · doi:10.1007/978-981-19-1689-2_11 · Cited by 5

User experience of self‐reported computerized medical history taking for acute chest pain: The Clinical Expert Operating System Chest Pain Danderyd Study

Sundberg, K. K., Adeli, A., Brandberg, H., Spaak, J., Koch, S., Sundberg, C. J., Zakim, D., Kahan, T., & Fritzell, K. (2022) · Health Expectations · doi:10.1111/hex.13612 · Cited by 5

Complex patients – an academism or reality?

Džakula, A., Lončarek, K., & Vočanec, D. (2023) · Croatian Medical Journal · doi:10.3325/cmj.2023.64.61 · Cited by 5

Diverging trajectories of trust in healthcare and on-line information seeking: what’s next with LLMs

Heumann, R. A., & Steinhubl, S. R. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02408-9 · Cited by 4

Revolutionizing Healthcare: Unleashing the Power of Machine Learning for Patient-Centric Solutions

Domadiya, D. H. (2024) · Shodh Sari-An International Multidisciplinary Journal · doi:10.59231/sari7688 · Cited by 4

Can unsupervised machine learning gain new insights into urodynamic pressure flow pattern analysis?

van Dort, W., Rosier, P. F. W. M., van Steenbergen, T. R. F., Geurts, B. J., & de Kort, L. M. O. (2025) · British Journal of Urology · doi:10.1111/bju.70011 · Cited by 4

Cardiovascular risk assessment enhanced by automated machine learning in a multi-phase study

Bibi, I., Schaffert, D., Blanke, P., Illian, L., Lenzing, F., Martin, N., Leipe, J., März, W., Stach, K., & Olsavszky, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-24189-z · Cited by 4

Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives

Chihara, D., Nastoupil, L. J., & Flowers, C. R. (2023) · British Journal of Haematology · doi:10.1111/bjh.18861 · Cited by 4

Social bias in artificial intelligence algorithms designed to improve cardiovascular risk assessment relative to the Framingham Risk Score: a protocol for a systematic review

Garcha, I., & Phillips, S. P. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-067638 · Cited by 9

The role of artificial intelligence in knowledge management for medical students and doctors

Luke, W. A. N. V., & Hooi, S. C. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2336065 · Cited by 4

PainAPPle. Validación y evaluación de una aplicación electrónica para el manejo del dolor agudo en pacientes pediátricos

García, E. M., Escudero, P. C., Mateos-Arroyo, J. A., Ramos-Luengo, A., Sánchez‐Alonso, F., & Barbero, F. R. (2019) · Revista Española de Anestesiología y Reanimación · doi:10.1016/j.redar.2019.09.004 · Cited by 4

Evolution of Clinical Medicine: From Expert Opinion to Artificial Intelligence

Barracca, A., Contini, M. C., Ledda, S., Mancosu, G., Pintore, G., KASHANI, K. B., & Ronco, C. (2020) · Journal of Translational Critical Care Medicine · doi:10.4103/jtccm.jtccm_6_21 · Cited by 4

Opening Editorial: Selection and Recruitment in Medical Education

Patterson, F., Griffin, B., & Hanson, M. D. (2018) · MedEdPublish · doi:10.15694/mep.2018.0000222.1 · Cited by 4

Propose and Review: Practical Bias Mitigation for Machine ClassificationPreprint

Li, T., Tang, Z., Lu, T., & Zhang, X. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4139244 · Cited by 5

The impact of automatic history-taking software on data quality in the cardiology outpatient clinic: Retrospective observational study

Erden, İ., Sen, A., & Erden, I. E. (2024) · Digital Health · doi:10.1177/20552076241260155 · Cited by 3

Stay Human or Go Machine? The Fate of Human Judgement in AI

Toscani, G. (2025) · Global Philosophy · doi:10.1007/s10516-025-09769-y · Cited by 3

A Predictive Model for Acute Admission in Aged Population

Mansourvar, M., Andersen‐Ranberg, K., Nøhr, C. G., & Wiil, U. K. (2018) · Studies in health technology and informatics · doi:10.3233/978-1-61499-852-5-96 · Cited by 3

An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

Gao, P., Perkowski, M. A., Li, Y., & Song, X. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.020914 · Cited by 4

Digital systems for improving outcomes in patients with primary immune defects

Rider, N. L. (2020) · Current Opinion in Pediatrics · doi:10.1097/mop.0000000000000963 · Cited by 3

Artificial Intelligence and Big Data in Medicine: scenarios, opportunities and critical issues

Musacchio, N. (2018) · The Journal of AMD · doi:10.36171/jamd18.21.3.04 · Cited by 4

Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol

Leonard, F., O’Sullivan, D. M., Gilligan, J., O’Shea, N., & Barrett, M. J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0294231 · Cited by 3

Design, implementation and evaluation of a spiral module combining data science, digital health and evidence-based medicine in the undergraduate medical curriculum: A mixed methods study

Al-Shakarchi, N. J., Upadhyay, J., Beckley, I., Gishen, F., Iorio, A. D., Stephens, R. C. M., Clegg, S., Lampe, F. C., & Banerjee, A. (2024) · Clinical Medicine · doi:10.1016/j.clinme.2024.100207 · Cited by 3

Addressing the Challenge of Missing Medical Data in Healthcare Analytics: A Focus on Machine Learning Predictions for ICU Length of Stay

Karankot, M. I., Marceau, M., Glenn, E. M., Fowers, R., Hedges, D. M., Sheehey, B., & Whitaker, B. M. (2024) · Intermountain Engineering, Technology and Computing (IETC) · doi:10.1109/ietc61393.2024.10564463 · Cited by 3

Challenges, Limitations, and Ethical Considerations of AI in Immunology and Healthcare

Rajchenberg, D., Hess, R., Lins, M. P., Manor, A., Degany, O., & Shoenfeld, Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch014 · Cited by 3

Big Data Analytics in Healthcare: A Review of Opportunities and Challenges

Mansourvar, M., Wiil, U. K., & Nøhr, C. G. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-60036-5_9 · Cited by 3

Sodium: sign, signifier, or signified, of sepsis?

O’Sullivan, M., & McCarthy, K. F. (2020) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2020.12.002 · Cited by 3

Modeling Drivers of Machine Learning in Health care Using Interpretive Structural Modeling Approach

Gupta, P., & Mehra, R. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-9829-6_35 · Cited by 3

AIM in Nursing Practice

Meetoo, D. D., & Ochieng, B. M. N. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_334 · Cited by 3

A machine learning framework for performing binary classification on tabular biomedical data

Szijártó, Á., Fábián, A., Lakatos, B. K., Tolvaj, M., Merkely, B. P., Kovács, A. D., & Tokodi, M. (2023) · Imaging · doi:10.1556/1647.2023.00109 · Cited by 3

Comparação da performance de algoritmos de machine learning para a análise preditiva em saúde pública e medicina

dos Santos, H. G. (2018) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.6.2018.tde-09102018-132826 · Cited by 5

Big data are here to stay!

Vincent, J., & Créteur, J. (2019) · Anaesthesia Critical Care & Pain Medicine · doi:10.1016/j.accpm.2019.05.001 · Cited by 3

Editorial: Precision dentistry and ehealth in oral healthcare

Joda, T., & Spallek, H. (2023) · Frontiers in Oral Health · doi:10.3389/froh.2023.1155166 · Cited by 3

Reexplicarnos la seguridad del paciente en Medicina Intensiva: necesidad de entender y actuar

Delgado, M. C. M., Fernández, M. C., Sirgo, G., & Bodí, M. A. (2025) · Medicina Intensiva · doi:10.1016/j.medin.2025.502219 · Cited by 2

Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images

Vidal‐Alaball, J., Bonache, A. A., Solé‐Casals, J., Fibla, D. R., Marin-Gomez, F. X., Distéfano, L., Boixadera, A., Casado-García, Á., García-Domínguez, M., Inés, A., et al. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1754682 · Cited by 9

Validation of an Automated AI Algorithm for the Quantification of Major OCT Parameters in Retinal Vein Occlusion–Related Macular Edema

Carnevali, A., Chisari, D., Gioia, R., Mancini, A., Borselli, M., Macrì, R., Lucisano, A., Scalzo, G. C., Frizziero, L., Scorcia, V., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15103561 · Cited by 2

Garbage in, garbage forever: why data needs a cold-chain mentality

Toscani, G. (2026) · Transforming Government People Process and Policy · doi:10.1108/tg-01-2026-0021 · Cited by 2

A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening

Tavakoli, M., & Kelley, P. (2021) · Institute of Physics eBooks · doi:10.1088/978-0-7503-2060-3ch12 · Cited by 2

El sobrediagnóstico en la práctica de la pediatría

(2020) · Archivos Argentinos de Pediatria · doi:10.5546/aap.2020.82 · Cited by 2

Multi-omics/genomics in predictive and personalized medicine

Ahmed, Z. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-91819-0.00005-1 · Cited by 2

Neuro-Symbolic AI for Supporting Chronic Disease Diagnosis and Monitoring

Magnini, M., Ciatto, G., Kuru, E., Sirocchi, C., & Montagna, S. (2025) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops65533.2025.00106 · Cited by 2

Integrating minds: adaptive knowledge sharing strategies for ML team synergy

Toscani, G. (2025) · Cognition Technology & Work · doi:10.1007/s10111-025-00822-9 · Cited by 2

Clinician Cognition and Artificial Intelligence in Medicine

Chang, A. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-823337-5.00007-x · Cited by 4

Rule-based decision support systems for eHealth

Schneider, P., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00015-8 · Cited by 2

Artificial Intelligence in Clinical Immunology

Chin, A. T., & Rider, N. L. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_83 · Cited by 2

L’intelligence artificielle dans les structures d’urgences : place de la formation et de la garantie humaine

Arnaud, É., Petitprez, E., Ammirati, C., Némitz, B., Dequen, G., Gignon, M., & Ghazali, D. A. (2023) · Annales françaises de médecine d’urgence · doi:10.3166/afmu-2022-0496 · Cited by 2

A Psychiatry Clerkship Orientation Based on Bite-Sized Teaching and Chalk Talks

Hish, A. J. (2024) · Academic Psychiatry · doi:10.1007/s40596-024-02010-9 · Cited by 2

Saving Lives With Algorithm-Enabled Process Innovation for Sepsis CarePreprint

Adjerid, I., Ayvaci, M., & Özer, Ö. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3456870 · Cited by 2

Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach

Anijdan, S. H. M., Moslemi, D., Reiazi, R., Tafti, H. F., Moghadamnia, A. A., & Paydar, R. (2024) · Journal of Medical Signals & Sensors · doi:10.4103/jmss.jmss_46_23 · Cited by 1

A Machine Learning Model for Predicting Depression in Moroccan Rheumatoid Arthritis Patients

Chakri, I., Qarmiche, N., Omari, M., Tachfouti, N., Fakir, S. E., & Otmani, N. (2025) · Cureus · doi:10.7759/cureus.80723 · Cited by 1

Predictive algorithms in healthcare: constituting ‘Artificial Intelligence’ (AI) as near human

Gjødsbøl, I. M. (2025) · BioSocieties · doi:10.1057/s41292-025-00374-1 · Cited by 3

Toward Conceptual and Functional Clarity of Autonomous Health Workforce Technologies: A Scoping Review

Ang, C., Ito, S., Yeow, A. Y. K., & Cleland, J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2598673 · Cited by 1

Aprendizaje automático aplicado en área de la salud. Parte 1

Quiroz, N. H., Posadas‐Martínez, M. L., Rossi, E., Giunta, D. H., & Risk, M. R. (2021) · Revista del Hospital Italiano de Buenos Aires · doi:10.51987/revhospitalbaires.v41i4.131 · Cited by 1

Implementation of Artificial Intelligence in Primary Care

Yang, H. (2023) · Korean Journal of Family Practice · doi:10.21215/kjfp.2023.13.2.73 · Cited by 1

Application of Genetic Algorithm in Predicting Mental Illness: A Case Study of Schizophrenia

Rabipour, S., & Asadi, Z. (2023) · Springer tracts in nature-inspired computing · doi:10.1007/978-981-99-3428-7_7 · Cited by 1

Promotion of the Critical-Reflective Use of Online Media Among Medical Students in Final Clinical Year

Kohmer, A., Zlatkin‐Troitschanskaia, O., Harendza, S., Jünger, J., Espe, L., Klose, V., & Demmer, I. (2024) · Springer eBooks · doi:10.1007/978-3-031-69510-0_8 · Cited by 1

A Fatal Case of Undiagnosed Candida Meningitis

Finelli, P. F. (2018) · The Neurologist · doi:10.1097/nrl.0000000000000186 · Cited by 1

The US Health Provider Workforce: Determinants and Potential Paths to Enhancement

Flier, J. S., & Rhoads, J. M. (2020) · Perspectives in biology and medicine · doi:10.1353/pbm.2020.0053 · Cited by 1

Artificial Intelligence Application to Microbiomics Data for Improved Clinical Decision Making in Precision Oncology

Mbatha, S. Z., Mkhize‐Kwitshana, Z. L., Mulaudzi, T. V., Hull, R., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_8 · Cited by 1

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

What About the Future?

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

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

COMPUTERS AND THE PRACTICE OF MEDICINE

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