The epic sepsis model falls short-the importance of external validation

Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014.

Rhee, C., Dantes, R., Epstein, L., Murphy, D. J., Seymour, C. W., Iwashyna, T. J., Kadri, S. S., Angus, D. C., Danner, R. L., Fiore, A. E., et al. (2017) · JAMA · doi:10.1001/jama.2017.13836 · Cited by 1,884 · see citing works

External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients

Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer-Cooley, O., Pestrue, J., Phillips, M., Konye, J., Penoza, C., et al. (2021) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2021.2626 · Cited by 995 · see citing works

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

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Validation of the Atherosclerotic Cardiovascular Disease Pooled Cohort Risk Equations

Muntner, P., Colantonio, L. D., Cushman, M., Goff, D. C., Howard, G., Howard, V. J., Kissela, B., Levitan, E. B., Lloyd-Jones, D. M., & Safford, M. M. (2014) · JAMA · doi:10.1001/jama.2014.2630 · Cited by 603 · see citing works

Principles for high-quality, high-value testing

Power, M., Fell, G., & Wright, M. (2013) · Evidence Based Medicine · doi:10.1136/eb-2012-100645 · Cited by 394 · see citing works

Accuracy of the Epic Sepsis Prediction Model in a Regional Health System

Bennett, T., Russell, S., King, J., Schilling, L., Voong, C., Rogers, N., Adrian, B., Bruce, N., & Ghosh, D. (2019) · arXiv · doi:10.48550/arxiv.1902.07276 · Cited by 19 · see citing works

Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence

von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M. A., et al. (2022) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2021.104153 · Cited by 360 · see citing works

AI pitfalls and what not to do: mitigating bias in AI

Gichoya, J. W., Thomas, K., Celi, L. A., Safdar, N., Banerjee, I., Banja, J. D., Seyyed-Kalantari, L., Trivedi, H., & Purkayastha, S. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20230023 · Cited by 277 · see citing works

AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence

Angus, D. C., Khera, R., Lieu, T., Liu, V., Ahmad, F. S., Anderson, B., Bhavani, S. V., Bindman, A., Brennan, T., Celi, L. A., et al. (2025) · JAMA · doi:10.1001/jama.2025.18490 · Cited by 195 · see citing works

External validation of AI models in health should be replaced with recurring local validation

Youssef, A., Pencina, M., Thakur, A., Zhu, T., Clifton, D., & Shah, N. H. (2023) · Nature Medicine · doi:10.1038/s41591-023-02540-z · Cited by 170 · see citing works

Sepsis biomarkers and diagnostic tools with a focus on machine learning

Komorowski, M., Green, A., Tatham, K. C., Seymour, C. W., & Antcliffe, D. B. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104394 · Cited by 160 · see citing works

From real-world electronic health record data to real-world results using artificial intelligence

Knevel, R., & Liao, K. P. (2022) · Annals of the Rheumatic Diseases · doi:10.1136/ard-2022-222626 · Cited by 163 · see citing works

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

Woodman, R. J., & Mangoni, A. A. (2023) · Aging Clinical and Experimental Research · doi:10.1007/s40520-023-02552-2 · Cited by 142 · see citing works

Evaluating the use of large language models to provide clinical recommendations in the Emergency Department

Williams, C. Y. K., Miao, B. Y., Kornblith, A. E., & Butte, A. J. (2024) · Nature Communications · doi:10.1038/s41467-024-52415-1 · Cited by 119 · see citing works

Predicting sepsis using deep learning across international sites: a retrospective development and validation study

Moor, M., Bennett, N., Plečko, D., Horn, M., Rieck, B., Meinshausen, N., Bühlmann, P., & Borgwardt, K. (2023) · eClinicalMedicine · doi:10.1016/j.eclinm.2023.102124 · Cited by 118 · see citing works

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

Zhang, S., Yu, J., Xu, X., Yin, C., Lu, Y., Yao, B., Tory, M., Padilla, L. M., Caterino, J., Zhang, P., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642343 · Cited by 99 · see citing works

Sepsis prediction model for determining sepsis vs SIRS, qSOFA, and SOFA

Schertz, A. R., Lenoir, K. M., Bertoni, A. G., Levine, B. J., Mongraw-Chaffin, M., & Thomas, K. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.29729 · Cited by 78 · see citing works

Transparency of artificial intelligence/machine learning-enabled medical devices

Shick, A. A., Webber, C. M., Kiarashi, N., Weinberg, J. P., Deoras, A., Petrick, N., Saha, A., & Diamond, M. C. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00992-8 · Cited by 67 · see citing works

Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions

Taylor, R. A., Sangal, R. B., Smith, M. E., Haimovich, A. D., Rodman, A., Iscoe, M. S., Pavuluri, S. K., Rose, C., Janke, A. T., Wright, D. S., et al. (2025) · Academic Emergency Medicine · doi:10.1111/acem.15066 · Cited by 62 · see citing works

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study

Kamran, F., Tang, S., Otles, E., McEvoy, D. S., Saleh, S. N., Gong, J., Li, B. Y., Dutta, S., Liu, X., Medford, R. J., et al. (2022) · BMJ · doi:10.1136/bmj-2021-068576 · Cited by 60 · see citing works

Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: A qualitative study

Joshi, M., Mecklai, K., Rozenblum, R., & Samal, L. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac022 · Cited by 46 · see citing works

Artificial intelligence for telemedicine diabetic retinopathy screening: a review

Nakayama, L. F., Ribeiro, L. Z., Novaes, F. D. C., Miyawaki, I. A., Miyawaki, A. E., de Oliveira, J. A. E., de Oliveira, T. V. F., Malerbi, F. K., Regatieri, C. V., Celi, L. A., et al. (2023) · Annals of Medicine · doi:10.1080/07853890.2023.2258149 · Cited by 49 · see citing works

Recommendations for achieving interoperable and shareable medical data in the USA

Szarfman, A., Levine, J. G., Tonning, J. M., Weichold, F., Bloom, J. C., Soreth, J. M., Geanacopoulos, M., Callahan, L., Spotnitz, H. M., Ryan, Q. C., et al. (2022) · Communications Medicine · doi:10.1038/s43856-022-00148-x · Cited by 47 · see citing works

Digital Determinants of Health: Health data poverty amplifies existing health disparities—A scoping review

Paik, K., Hicklen, R., Kaggwa, F., Puyat, C. V. M., Nakayama, L. F., Ong, B., Shropshire, J., & Villanueva, C. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000313 · Cited by 44 · see citing works

Overcoming Pilotitis in Digital Medicine at the Intersection of Data, Clinical Evidence, and Adoption

Egermark, M., Blasiak, A., Remus, A., Sapanel, Y., & Ho, D. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200056 · Cited by 38 · see citing works

Why do probabilistic clinical models fail to transport between sites

Lasko, T. A., Strobl, E. V., & Stead, W. W. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01037-4 · Cited by 34 · see citing works

Tailoring Risk Prediction Models to Local Populations

Zinzuwadia, A. N., Mineeva, O., Li, C., Farukhi, Z. M., Giulianini, F., Cade, B. E., Chen, L., Karlson, E. W., Paynter, N. P., Mora, S., et al. (2024) · JAMA Cardiology · doi:10.1001/jamacardio.2024.2912 · Cited by 29 · see citing works

Autonomy by Design: Preserving Human Autonomy in AI Decision-Support

Buijsman, S., Carter, S. E., & Bermúdez, J. P. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00932-2 · Cited by 28 · see citing works

DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence eraPreprint

Restrepo, D., Wu, C.-W., Vásquez-Venegas, C., Nakayama, L., Celi, L., & Lopez, D. M. (2024) · Research Square · doi:10.21203/rs.3.rs-4277992/v1 · Cited by 25 · see citing works

Prospective Human Validation of Artificial Intelligence Interventions in Cardiology

Moosavi, A., Huang, S., Vahabi, M., Motamedivafa, B., Tian, N., Mahmood, R., Liu, P. X., & Sun, C. L. F. (2024) · JACC Advances · doi:10.1016/j.jacadv.2024.101202 · Cited by 24 · see citing works

Performance of Machine Learning Suicide Risk Models in an American Indian Population

Haroz, E. E., Rebman, P., Goklish, N. A., Garcia, M., Suttle, R., Maggio, D., Clattenburg, E. J., Mega, J., & Adams, R. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.39269 · Cited by 23 · see citing works

Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models

Schinkel, M., Bennis, F. C., Boerman, A. W., Wiersinga, W. J., & Nanayakkara, P. W. B. (2023) · Scientific Reports · doi:10.1038/s41598-023-35557-y · Cited by 20 · see citing works

Toward governance of artificial intelligence in pediatric healthcare

Richter, F., Holmes, E., Richter, F., Guttmann, K., Duong, S. Q., Gangadharan, S., Schadt, E., Salmasian, H., Gelb, B. D., & Glicksberg, B. S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02000-7 · Cited by 24 · see citing works

External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems

Cummings, B. C., Blackmer, J. M., Motyka, J. R., Farzaneh, N., Cao, L., Bisco, E. L., Glassbrook, J. D., Roebuck, M. D., Gillies, C. E., Admon, A. J., et al. (2023) · Critical Care Medicine · doi:10.1097/ccm.0000000000005837 · Cited by 19 · see citing works

Possibilities and challenges for artificial intelligence and machine learning in perioperative care

van der Meijden, S. L., Arbous, M. S., & Geerts, B. F. (2023) · BJA Education · doi:10.1016/j.bjae.2023.04.003 · Cited by 20 · see citing works

The Opportunities and Challenges for Artificial Intelligence to Improve Sepsis Outcomes in the Paediatric Intensive Care Unit

Aslan, A. T., Permana, B., Harris, P. N. A., Naidoo, K. D., Pienaar, M. A., & Irwin, A. (2023) · Current Infectious Disease Reports · doi:10.1007/s11908-023-00818-4 · Cited by 17 · see citing works

Multi-Evidence Clinical Reasoning With Retrieval-Augmented Generation for Emergency Triage: Retrospective Evaluation Study

Wong, H. S., & Wong, T. K. (2026) · JMIR Medical Informatics · doi:10.2196/82026 · Cited by 16 · see citing works

A scoping review of the landscape of health-related open datasets in Latin America

Restrepo, D. S., Quion, J. M., Vásquez-Venegas, C., Villanueva, C., Celi, L. A., & Nakayama, L. F. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000368 · Cited by 18 · see citing works

Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review

Nakayama, L. F., de Matos, J. C. R. G., Quion, J., Novaes, F. D. C., Mitchell, W. G., Mwavu, R., Hung, C. J.-Y. J., Santiago, A. P. D., Phanphruk, W., Cardoso, J. S., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000618 · Cited by 19 · see citing works

Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings

Nguyen, K. A., Wilson, D. L., Diiulio, J., Hall, B., Militello, L. G., Gellad, W. F., Harle, C. A., Lewis, M. O., Schmidt, S. O., Rosenberg, E. I., et al. (2024) · Bioelectronic Medicine · doi:10.1186/s42234-024-00156-3 · Cited by 13 · see citing works

Complete Blood Count and Monocyte Distribution Width–Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study

Campagner, A., Agnello, L., Carobene, A., Padoan, A., Ben, F. D., Locatelli, M., Plebani, M., Ognibene, A., Lorubbio, M., Vecchi, E. D., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/55492 · Cited by 16 · see citing works

External validation of the Epic sepsis predictive model in 2 county emergency departments.

Ostermayer, D. G., Braunheim, B., Mehta, A. M., Ward, J., Andrabi, S., & Sirajuddin, A. M. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae133 · Cited by 13 · see citing works

Development and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): a multicentre study using electronic health record data

van der Meijden, S. L., van Boekel, A. M., Schinkelshoek, L., van Goor, H., Steyerberg, E. W., Nelissen, R. G. H. H., Mesotten, D., Geerts, B. F., de Boer, M. G. J., Arbous, M. S., et al. (2024) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2024.101163 · Cited by 13 · see citing works

Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study

Lu, S.-C., Chen, G. H., Liu, A.-S., Sun, J., Gao, J.-W., Huang, C., Tsai, C., & Fu, L. (2025) · Journal of Medical Internet Research · doi:10.2196/67576 · Cited by 13 · see citing works

Fairness and generalizability of OCT normative databases: a comparative analysis

Nakayama, L. F., Ribeiro, L. Z., de Oliveira, J. A. E., de Matos, J. C. R. G., Mitchell, W. G., Malerbi, F. K., Celi, L. A., & Regatieri, C. V. (2023) · International Journal of Retina and Vitreous · doi:10.1186/s40942-023-00459-8 · Cited by 12 · see citing works

Application of the KA-Transformer model to early sepsis prediction: a hybrid network analysis based on time series data

Zhu, Y., Cheng, Y., Zhang, T., Li, Z., Hong, X., Wang, D., & Xiao, J. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06628-8 · Cited by 12 · see citing works

A Clinician’s Guide to Understanding Bias in Critical Clinical Prediction Models

de Matos, J. C. R. G., Gallifant, J., Chowdhury, A., Economou-Zavlanos, N., Charpignon, M., Gichoya, J. W., Celi, L. A., Nazer, L. H., King, H., & Wong, A.-K. I. (2024) · Critical Care Clinics · doi:10.1016/j.ccc.2024.05.011 · Cited by 9 · see citing works

From theory to practice: Harmonizing taxonomies of trustworthy AI

Makridis, C. A., Mueller, J. M., Tiffany, T., Borkowski, A., Zachary, J. M., & Alterovitz, G. (2024) · Health Policy OPEN · doi:10.1016/j.hpopen.2024.100128 · Cited by 12 · see citing works

Clinical Impact of a Sepsis Alert System Plus Electronic Sepsis Navigator Using the Epic Sepsis Prediction Model in the Emergency Department

Schertz, A. R., Smith, S. A., Lenoir, K. M., & Thomas, K. W. (2023) · Journal of Emergency Medicine · doi:10.1016/j.jemermed.2023.02.025 · Cited by 8 · see citing works

ICU command centres in critical care: Nursing workflows, organizational models, and implementation challenges. A narrative review

Muñoz, J., Muñoz-Visedo, L., Moreno-Ortega, E., Ribón-Liberal, R., & Muñoz-Visedo, J. (2025) · Intensive and Critical Care Nursing · doi:10.1016/j.iccn.2025.104245 · Cited by 7 · see citing works

The progress of organ protection mechanisms in sepsis

Zhu, Y., Cheng, J., Sun, Z., Lu, J., & Li, M. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1729499 · Cited by 7 · see citing works

Artificial intelligence in emergency medicine: a narrative review

Rego, A., Arango-Ibañez, J. P., Taylor, R. A., Smith, M. E., Jones, D. D., Pelletier, J., Colletti, J. E., Gottlieb, M. A., & Long, B. (2026) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2026.01.028 · Cited by 7 · see citing works

Introduction to artificial intelligence for cardiovascular clinicians

Chang, A. C., & Limon, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00010-x · Cited by 11 · see citing works

Addressing the “elephant in the room” of AI clinical decision support through organisation-level regulation

Zhang, J., Mattie, H., Shuaib, H., Hensman, T., Teo, J., & Celi, L. A. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000111 · Cited by 7 · see citing works

Evaluating the use of GPT-3.5-turbo to provide clinical recommendations in the Emergency DepartmentPreprint

Williams, C. Y. K., Miao, B. Y., & Butte, A. J. (2023) · medRxiv · doi:10.1101/2023.10.19.23297276 · Cited by 7 · see citing works

An early pipeline framework for assessing vendor AI solutions to support return on investment

Binkley, C. E., Bouslov, D., Zaidi, A., Kaye, L., Whalen, R., Sethi, S., & Azar, J. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01767-z · Cited by 6 · see citing works

A joint physics and radiobiology DREAM team vision – Towards better response prediction models to advance radiotherapy

Vens, C., van Luijk, P., Vogelius, R. I., Naqa, I. E., Humbert‐Vidan, L., von Neubeck, C., Gomez-Roman, N., Bahn, E., Brualla, L., Böhlen, T. T., et al. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110277 · Cited by 6 · see citing works

Rebooting artificial intelligence for health

Mitchell, W. G., Gichoya, J. W., & Celi, L. A. (2025) · PLOS Global Public Health · doi:10.1371/journal.pgph.0004171 · Cited by 6 · see citing works

Artificial intelligence, data sharing, and privacy for retinal imaging under Brazilian Data Protection Law

Nakayama, L., Ribeiro, L. Z., Malerbi, F., & Regatieri, C. (2025) · International Journal of Retina and Vitreous · doi:10.1186/s40942-024-00596-8 · Cited by 11 · see citing works

Machine-Learning Applications in Thrombosis and Hemostasis

Nilius, H., & Nagler, M. (2024) · Hämostaseologie · doi:10.1055/a-2407-7994 · Cited by 6 · see citing works

Not the Models You Are Looking For: Traditional ML Outperforms LLMs in Clinical Prediction TasksPreprint

Brown, K. E., Yan, C., Li, Z., Zhang, X., Collins, B. X., Chen, Y., Clayton, E. W., Kantarcıoğlu, M., Vorobeychik, Y., & Malin, B. (2024) · medRxiv · doi:10.1101/2024.12.03.24318400 · Cited by 6 · see citing works

Algorithm-Based Clinical Decision Support: Evolving Regulatory Landscape and Best Practices for Local Oversight

Lin, A. L., Parrish, A. B., Cary, M. P., Silcox, C., Balu, S., Jelovsek, J. E., O’Brien, C. L., Pencina, M. J., Poon, E. G., & Economou-Zavlanos, N. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-094601 · Cited by 7 · see citing works

ICU readmission and mortality risk prediction: Generalizability of a multi-hospital model

Dam, T. A., de Bruin, D., Cinà, G., Thoral, P. J., Elbers, P., den Uil, C. A., & Crane, R. F. (2025) · Journal of Intensive Medicine · doi:10.1016/j.jointm.2025.03.007 · Cited by 5 · see citing works

Beyond algorithms: Ethical implications of AI in healthcare

Pathni, R. K. (2024) · Medical Journal Armed Forces India · doi:10.1016/j.mjafi.2024.10.014 · Cited by 3 · see citing works

Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit

Riahi, A., Yazdani, M. S., Eshraghi, R., Houyeh, M. K., Bahrami, A., Khoshdooz, S., Amini, M., Behzadi, E., Khalaji, A., Taba, S. M. M., et al. (2025) · Critical Care Research and Practice · doi:10.1155/ccrp/9031137 · Cited by 4 · see citing works

A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications

Liu, N., Han, G., Gu, Q., Zhang, Y., & Chen, M. M. (2026) · Cell Death and Disease · doi:10.1038/s41419-026-08769-z · Cited by 3 · see citing works

Artificial Intelligence

Nakayama, L. F., Ribeiro, L. Z., Ferraz, D., & Regatieri, C. V. (2024) · Springer eBooks · doi:10.1007/978-3-031-29358-0_16-1 · Cited by 3 · see citing works

The challenges of using electronic medical records (EMR) to facilitate guideline-directed medical therapy (GDMT) for patients with heart failure (HF) and chronic kidney disease (CKD)

Desai, N. R., Ahmad, T., & Wilson, F. P. (2025) · Heart Failure Reviews · doi:10.1007/s10741-025-10564-5 · Cited by 4 · see citing works

International Retrospective Observational Study of Continual Learning for AI on Endotracheal Tube Placement from Chest Radiographs

Chen, E., Saenz, A. D., Banerjee, O., Marklund, H., Zhang, X., Johri, S., Zhou, H.-Y., Luo, L., Adithan, S., Wu, K., et al. (2025) · NEJM AI · doi:10.1056/aioa2500522 · Cited by 2 · see citing works

AI agents in clinical medicine: delegation, supervision, and the problem of correlated failure

Waisberg, E., & Guarnieri, J. W. (2026) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-026-04584-9 · Cited by 2 · see citing works

AI ethics for the everyday intensivist

Tan, S. C., Modra, L. J., & Hensman, T. (2025) · Critical Care and Resuscitation · doi:10.1016/j.ccrj.2025.100115 · Cited by 2 · see citing works

Conceptual Framework for Prediction Models of Patient Deterioration Based on Nursing Documentation Patterns: Reproducibility and Generalizability with a Large Number of Hospitals Across the United States

Wan, Y.-K. J., Abdelrahman, S., Facelli, J. C., Madaras‐Kelly, K., Kawamoto, K., Dishman, D., Himes, S., Cato, K., Rossetti, S. C., & Fiol, G. D. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104887 · Cited by 2 · see citing works

Artificial Intelligence

Nakayama, L. F., Ribeiro, L. Z., Ferraz, D., & Regatieri, C. V. (2025) · Springer eBooks · doi:10.1007/978-3-031-29358-0_16-2 · Cited by 2 · see citing works

Large language models for predicting perioperative sepsis

Chen, Y., Zhang, L., Liu, J., & Bin, Y. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06741-6 · Cited by 1 · see citing works

Comparison of clinical outcomes of sepsis patients in two county emergency departments using systemic inflammatory response syndrome versus Epic’s proprietary severe sepsis alert

Ostermayer, D. G., Braunheim, B., Mehta, A., Ward, J., Andrabi, S., & Sirajuddin, A. M. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf207 · Cited by 1 · see citing works

A Multiagent Large Language Model Framework for Emergency Treatment Recommendation in Acute Ischemic Stroke: Development and Validation Study

Yan, B., Zhang, R., Chen, L., Song, X. Z., Cao, Z., & Li, Y. (2026) · Journal of Medical Internet Research · doi:10.2196/96304 · Cited by 1 · see citing works

Machine learning and medicine- a brief introduction

Lee, B. D., & Peterson, C. (2022) · The Southwest Respiratory and Critical Care Chronicles · doi:10.12746/swrccc.v10i45.1043 · Cited by 1 · see citing works

Comparison of Machine Learning Models for Predictive Analytics of Sepsis in the Emergency Medical Admissions

Vandana, & Hari, P. B. (2023) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca57980.2023.10220741

Patient Deterioration Prediction Models Using Nursing Documentation Patterns as Predictors: A Reproducibility and Generalizability Study Across a Large Number of Hospitals Across the United StatesPreprint

Wan, Y.-K. J., Abdelrahman, S., Facelli, J. C., Madaras‐Kelly, K., Kawamoto, K., Dishman, D., Himes, S., & Fiol, G. D. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4791318

Generalizing AI for eye diagnosis

Liu, X. (2024) · doi:10.32657/10356/179566

Comparing Single-Hospital and National Models to Predict 30-Day Inpatient Mortality

Cogill, S., Heberer, K. R., Kaushal, A., Fang, D. Z., & Lee, J. S. (2025) · Journal of General Internal Medicine · doi:10.1007/s11606-024-09315-3

Tadpole: A Framework for Characterizing Temporal Model Data Drifts at the Point-of-Care

Pungitore, S. A., & Subbian, V. (2024) · IEEE Healthcare Innovations and Point of Care Technologies Conference (HI-POCT) · doi:10.1109/hi-poct64255.2024.10876202

Rural Medical Centers Struggle to Produce Well-Calibrated Clinical Prediction Models: Data Augmentation Can HelpPreprint

Brown, K. E., Malin, B., & Davis, S. E. (2025) · medRxiv · doi:10.1101/2025.06.16.25329699

Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice

Isaksen, J. L., Nørregaard, M., Manninger, M., Dobrev, D., Jespersen, T., Hermans, B. J. M., Heijman, J., Plank, G., Scherr, D., Pock, T., et al. (2025) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2025.101783 · Cited by 2 · see citing works

Artificial Intelligence in U.S. Healthcare: Legal Framework, Liability, and Ethical Challenges with Special Focus on Pediatric CarePreprint

Ponce, M. E. T., & Arana, M. N. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5468846

From Anamnesis to Algorithm: A Clinical Framework for the Design of Artificial Intelligence Focused on Medical Workflow

Lerma-Torres, D. C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-09037-9_11

Overcoming the "valley of death" in medical research and development: relevance and key issues

Khrustalev, M. B., & Reznik, V. A. (2025) · Real-World Data & Evidence · doi:10.37489/2782-3784-myrwd-078

A hybrid-reasoner LLM framework toward real-world clinical decision- making support in acute ischemic strokePreprint

Yan, B.-C., Zhang, R., Fan, Y., Li, Y., Chen, L., Song, X., Tang, Y., Tu, Y., Cao, Z., Shen, L., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7998391/v1

Real-time Inflammation Panel for inpatient care: a before-after evaluation of post-analytic timeliness and outcomes at a tertiary centerPreprint

Georgy, K., Metsker, O., Stanislav, Y., Dmitry, K., Shelukhanov, N. K., Evgeny, K., Yury, M., & Alexey, Y. (2025) · Research Square · doi:10.21203/rs.3.rs-8105230/v1

Artificial Intelligence in Healthcare

Hughes, A. C. M., & McCradden, M. D. (2025) · Frontiers for Young Minds · doi:10.3389/frym.2025.1536394

Digital Medicine and Artificial Intelligence in Chronic Myeloid Leukemia: Current Applications, Challenges, and Future Directions

Asadov, C. D., Shirinova, A., Alimirzoyeva, Z., & Hasanova, A. (2026) · Cureus · doi:10.7759/cureus.103951 · Cited by 2 · see citing works

Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help

Brown, K. L., Malin, B., & Davis, S. E. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105012

Slik kan sykepleiere oppdage sepsis

Gustad, L. T., Sandsæter, H. L., Bangstad, I. L., Høvik, L. H., & Damås, J. K. (2026) · Sykepleien Forskning · doi:10.4220/sykepleiens.2026.102425

A multi-agent LLM framework toward real-world clinical decision-making support in acute ischemic stroke (Preprint)Preprint

Li, Y., Yan, B.-C., Zhang, R., Song, X. Z., Chen, L., & Cao, Z. (2026) · doi:10.2196/preprints.96304

From Expert Systems to Ambient and Augmented Care: AI in Internal Medicine, Dermatology, Neurology and Neurosurgery

Alrefaie, K., Madlol, B., & Atallah, O. (2026) · Education and human development · doi:10.5772/intechopen.1014649

From patient notes to prognostication: The revolutionary potential of event‐based foundation models in orthopaedics

Bouterse, A., Pruneski, J. A., Oettl, F. C., Zsidai, B., Longo, U. G., Tischer, T., Hirschmann, M. T., & Samuelsson, K. (2026) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1002/ksa.70428

Hand-off Zones in AI Accountability: Beyond Static-Gap FramingsPreprint

Saviano, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6886800

Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment

Zhu, M., Gou, L., Zhang, C., & Zheng, D. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1912362

MACHINE LEARNING IN PREDICTION OF SEPSIS IN INTENSIVE CARE UNITS: A LITERATURE REVIEW OF ALGORITHMIC MODELS AND CLINICAL IMPLEMENTATION

Parzniewski, M., Ostruszka, N., Marusińska, M., Karpiński, M., Czyz, W., Kolawa, S., Markiewicz, W., Borowiec, A., Pilecka, J., & Wojciechowski, J. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.3(51).2026.5768

Power, privilege and moral responsibility: learning from I.M. Young's Social Connection Model in the context of AI-driven healthcare

Ferlito, B., Proost, M. D., & Segers, S. (2026) · Bioethics News · doi:10.1007/s40592-026-00301-5

Transfer learning with deployment-covariate recalibration for survival prediction under covariate shift

Pan, L., Zhao, G., Yu, Y., & Qin, G. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01285-x

Large Language Models in Multidisciplinary Decision-Making for Hepatopancreatobiliary Oncology: Retrospective Comparative Feasibility Study

Jo, S. J., Chong, E. H., Kang, I., Yang, S. J., Kang, B., Kim, J. S., Chon, H. J., Kwon, C., Sung, M. J., Shin, S. P., et al. (2026) · Journal of Medical Internet Research · doi:10.2196/101137

Agentic AI in Medicine: Challenges for Responsible Development and the Case for Clinical Testing Harnesses

Waisberg, E., & Guarnieri, J. W. (2026) · Annals of Biomedical Engineering · doi:10.1007/s10439-026-04381-6

Retrieval-Augmented Generation System for Supporting Hospital Accreditation Preparation Using Policy Documents, Audit Findings, Quality Indicators, Regulatory Standards, and Departmental Evidence Files

Garcia, I., & Herrera, D. (2026) · Journal of Health Informatics and Digital Systems · doi:10.68159/q638866091

Artificial Intelligence

Nakayama, L. F., Ribeiro, L. Z., Ferraz, D., & Regatieri, C. S. (2026) · Springer eBooks · doi:10.1007/978-3-032-23052-2_16

Conformity, Assertion, or Compromise? A Scenario-Based Framework for Evaluating Autonomy-Relevant Response Patterns in LLM Advice

Haez, S. G., Consolandi, M., & Dragoni, M. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3838184

Use of Sepsis Clinical Prediction Models to Improve Patient Care

Mogri, M., Grant, R. W., & Liu, V. X. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2022.7164 · Cited by 4 · see citing works

Letter to Editor: “External validation of eight different models to predict sepsis mortality in intensive care units”

Kumaran, L., & Gupta, S. (2025) · Journal of Critical Care · doi:10.1016/j.jcrc.2025.155266

Letter to the editor: “External validation of eight different models to predict sepsis mortality in intensive care units”

Hu, Y., Guo, J., & Yao, W. (2025) · Journal of Critical Care · doi:10.1016/j.jcrc.2025.155267

Authors reply: "The external validation of eight different models to predict sepsis mortality in intensive care units is still valid"

Hargovan, S., Simpson, C., Sivalingam, S., Carter, A., & Gunnarsson, R. K. (2025) · Journal of Critical Care · doi:10.1016/j.jcrc.2025.155268

Authors reply: “External validation of eight different models to predict sepsis mortality in intensive care units”

Hargovan, S., Simpson, C., Sivalingam, S., Carter, A., & Gunnarsson, R. K. (2026) · Journal of Critical Care · doi:10.1016/j.jcrc.2026.155496