{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T09:06:07Z","timestamp":1776071167473,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.7759\/cureus.106934","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T08:24:22Z","timestamp":1776068662000},"source":"Crossref","is-referenced-by-count":0,"title":["Smart ICUs: The Role of Artificial Intelligence in Modern Intensive Care Units"],"prefix":"10.7759","author":[{"given":"Nikita","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.7861\/clinmedicine.14-4-376","article-title":"Intensive care medicine is 60 years old: the history and future of the intensive care unit","volume":"14","author":"Kelly FE","year":"2014","unstructured":"Kelly FE, Fong K, Hirsch N, Nolan JP. Intensive care medicine is 60 years old: the history and future of the intensive care unit. Clin Med (Lond). 2014, 14:376-9. 10.7861\/clinmedicine.14-4-376","journal-title":"Clin Med (Lond)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2020.06.040","article-title":"History of artificial intelligence in medicine","volume":"92","author":"Kaul V","year":"2020","unstructured":"Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020, 92:807-12. 10.1016\/j.gie.2020.06.040","journal-title":"Gastrointest Endosc"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-20910-4","article-title":"Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare","volume":"12","author":"Goh KH","year":"2021","unstructured":"Goh KH, Wang L, Yeow AY, Poh H, Li K, Yeow JJ, Tan GY. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun. 2021, 12:711. 10.1038\/s41467-021-20910-4","journal-title":"Nat Commun"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102036","article-title":"DeepAISE - an interpretable and recurrent neural survival model for early prediction of sepsis","volume":"113","author":"Shashikumar SP","year":"2021","unstructured":"Shashikumar SP, Josef CS, Sharma A, Nemati S. DeepAISE - an interpretable and recurrent neural survival model for early prediction of sepsis. Artif Intell Med. 2021, 113:102036. 10.1016\/j.artmed.2021.102036","journal-title":"Artif Intell Med"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-022-03916-2","article-title":"Artificial intelligence in infection management in the ICU","volume":"26","author":"De Corte T","year":"2022","unstructured":"De Corte T, Van Hoecke S, De Waele J. Artificial intelligence in infection management in the ICU. Crit Care. 2022, 26:79. 10.1186\/s13054-022-03916-2","journal-title":"Crit Care"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.2196\/70674","article-title":"AI predictive model of mortality and intensive care unit admission in the COVID-19 pandemic: retrospective population cohort study of 12,000 patients","volume":"27","author":"Ruiz Giardin JM","year":"2025","unstructured":"Ruiz Giardin JM, Garnica \u00d3, Mesa Plaza N, et al.. AI predictive model of mortality and intensive care unit admission in the COVID-19 pandemic: retrospective population cohort study of 12,000 patients. J Med Internet Res. 2025, 27:e70674. 10.2196\/70674","journal-title":"J Med Internet Res"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1177\/29768675241298918","article-title":"Artificial intelligence for mechanical ventilation: a transformative shift in critical care","volume":"19","author":"Misseri G","year":"2024","unstructured":"Misseri G, Piattoli M, Cuttone G, Gregoretti C, Bignami EG. Artificial intelligence for mechanical ventilation: a transformative shift in critical care. Ther Adv Pulm Crit Care Med. 2024, 19:29768675241298918. 10.1177\/29768675241298918","journal-title":"Ther Adv Pulm Crit Care Med"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1177\/20552076251352988","article-title":"Predicting mechanical ventilation duration in ICU patients: a data-driven machine learning approach for clinical decision-making","volume":"11","author":"Mendiratta S","year":"2025","unstructured":"Mendiratta S, Mukkelli VG, Baidya Kayal E, Khanna P, Mehndiratta A. Predicting mechanical ventilation duration in ICU patients: a data-driven machine learning approach for clinical decision-making. Digit Health. 2025, 11:20552076251352988. 10.1177\/20552076251352988","journal-title":"Digit Health"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrtlng.2022.11.005","article-title":"Developing an explainable machine learning model to predict the mechanical ventilation duration of patients with ARDS in intensive care units","volume":"58","author":"Wang Z","year":"2023","unstructured":"Wang Z, Zhang L, Huang T, et al.. Developing an explainable machine learning model to predict the mechanical ventilation duration of patients with ARDS in intensive care units. Heart Lung. 2023, 58:74-81. 10.1016\/j.hrtlng.2022.11.005","journal-title":"Heart Lung"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.935366","article-title":"An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: a two-stage prediction approach","volume":"9","author":"Liu CF","year":"2022","unstructured":"Liu CF, Hung CM, Ko SC, et al.. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: a two-stage prediction approach. Front Med (Lausanne). 2022, 9:935366. 10.3389\/fmed.2022.935366","journal-title":"Front Med (Lausanne)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2024.104648","article-title":"Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models","volume":"154","author":"Tan Y","year":"2024","unstructured":"Tan Y, Dede M, Mohanty V, Dou J, Hill H, Bernstam E, Chen K. Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models. J Biomed Inform. 2024, 154:104648. 10.1016\/j.jbi.2024.104648","journal-title":"J Biomed Inform"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1186\/s44158-023-00125-3","article-title":"Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit","volume":"3","author":"Fragasso T","year":"2023","unstructured":"Fragasso T, Raggi V, Passaro D, Tardella L, Lasinio GJ, Ricci Z. Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit. J Anesth Analg Crit Care. 2023, 3:37. 10.1186\/s44158-023-00125-3","journal-title":"J Anesth Analg Crit Care"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.964894","article-title":"Predicting acute kidney injury risk in acute myocardial infarction patients: an artificial intelligence model using Medical Information Mart for Intensive Care databases","volume":"9","author":"Cai D","year":"2022","unstructured":"Cai D, Xiao T, Zou A, et al.. Predicting acute kidney injury risk in acute myocardial infarction patients: an artificial intelligence model using Medical Information Mart for Intensive Care databases. Front Cardiovasc Med. 2022, 9:964894. 10.3389\/fcvm.2022.964894","journal-title":"Front Cardiovasc Med"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1097\/ALN.0000000000002300","article-title":"Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis","volume":"129","author":"Hatib F","year":"2018","unstructured":"Hatib F, Jian Z, Buddi S, et al.. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology. 2018, 129:663-74. 10.1097\/ALN.0000000000002300","journal-title":"Anesthesiology"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1111\/echo.15714","article-title":"Artificial intelligence calculated global longitudinal strain and left ventricular ejection fraction predicts cardiac events and all-cause mortality in patients with chest pain","volume":"40","author":"O'Driscoll JM","year":"2023","unstructured":"O'Driscoll JM, Tuttolomondo D, Gaibazzi N. Artificial intelligence calculated global longitudinal strain and left ventricular ejection fraction predicts cardiac events and all-cause mortality in patients with chest pain. Echocardiography. 2023, 40:1356-64. 10.1111\/echo.15714","journal-title":"Echocardiography"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1111\/echo.15904","article-title":"Automatic tablet-based monoplane quantification of stroke volume and left ventricular ejection fraction: a comparative assessment against computer-based biplane and monoplane tools","volume":"41","author":"de Raat FM","year":"2024","unstructured":"de Raat FM, Bingley P, Bouwmeester S, Felix SE, Montenij LJ, Bouwman AR. Automatic tablet-based monoplane quantification of stroke volume and left ventricular ejection fraction: a comparative assessment against computer-based biplane and monoplane tools. Echocardiography. 2024, 41:e15904. 10.1111\/echo.15904","journal-title":"Echocardiography"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.2196\/35293","article-title":"Comparison of severity of illness scores and artificial intelligence models that are predictive of intensive care unit mortality: meta-analysis and review of the literature","volume":"10","author":"Barboi C","year":"2022","unstructured":"Barboi C, Tzavelis A, Muhammad LN. Comparison of severity of illness scores and artificial intelligence models that are predictive of intensive care unit mortality: meta-analysis and review of the literature. JMIR Med Inform. 2022, 10:e35293. 10.2196\/35293","journal-title":"JMIR Med Inform"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2017.10.002","article-title":"Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach","volume":"108","author":"Awad A","year":"2017","unstructured":"Awad A, Bader-El-Den M, McNicholas J, Briggs J. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Int J Med Inform. 2017, 108:185-95. 10.1016\/j.ijmedinf.2017.10.002","journal-title":"Int J Med Inform"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.amsu.2016.09.002","article-title":"Using electronic health record collected clinical variables to predict medical intensive care unit mortality","volume":"11","author":"Calvert J","year":"2016","unstructured":"Calvert J, Mao Q, Hoffman JL, et al.. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond). 2016, 11:52-7. 10.1016\/j.amsu.2016.09.002","journal-title":"Ann Med Surg (Lond)"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly CJ","year":"2019","unstructured":"Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019, 17:195. 10.1186\/s12916-019-1426-2","journal-title":"BMC Med"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/482415-smart-icus-the-role-of-artificial-intelligence-in-modern-intensive-care-units","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T08:24:23Z","timestamp":1776068663000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/482415-smart-icus-the-role-of-artificial-intelligence-in-modern-intensive-care-units"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":20,"URL":"https:\/\/doi.org\/10.7759\/cureus.106934","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,13]]}}}