{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T19:53:07Z","timestamp":1793735587547,"version":"4.6.1"},"reference-count":32,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Nursing in Critical Care"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is reshaping clinical decision support systems (CDSSs). In acute and critical care, nurses provide continuous surveillance, recognise deterioration, coordinate escalation and translate protocols into bedside action. AI\u2010CDSS may be particularly relevant when they support rather than replace clinical judgement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To examine whether nurse\u2010used AI\u2010CDSS improve patient\u2010important outcomes in acute and critical care contexts and summarise effects on care processes and nurse\u2010reported outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Following PRISMA 2020 and a preregistered protocol, we searched eight databases and major trial registries for English\u2010language studies from 1 January 2010 to 1 January 2026. Searches were conducted on 1 January 2026. We included randomised, quasi\u2010experimental and adjusted cohort studies in which registered nurses or nursing teams were primary users of AI\u2010CDSS generating patient\u2010specific predictions or recommendations. Mortality was pooled using a random effects model; other outcomes were synthesised narratively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Seven studies involving about 75\u2009000 patients were included. Most evidence came from acute wards, intensive care units, sepsis, deterioration and delirium\u2010prevention contexts, with additional home and palliative care evidence. Three mortality studies were pooled. Nurse\u2010facing AI\u2010CDSS were associated with lower hospital mortality (RR 0.68, 95% CI 0.53\u20130.87;\n                      <jats:italic>I<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200924%), although the prediction interval included possible no effect. Length of stay and protocol adherence generally improved when tools were embedded in nursing workflows. Nurse\u2010reported outcomes were sparse.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Nurse\u2010facing AI\u2010CDSS may strengthen acute and critical care nursing by improving surveillance, escalation and protocol delivery for patients at risk of deterioration. Evidence is promising but limited by small study numbers, heterogeneous interventions and sparse nurse\u2010reported outcomes. Critical care implementation should prioritise nurse\u2010centred design, alert burden, equity, safety monitoring and rigorous evaluation before scale\u2010up.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance to Clinical Practice<\/jats:title>\n                    <jats:p>Nurse\u2010used AI\u2010CDSS show potential to improve patient outcomes and care processes, but evidence remains limited and context dependent.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/nicc.70606","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T06:11:52Z","timestamp":1786083112000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Harnessing Artificial Intelligence to Strengthen Acute and Critical Care Nursing Practice: A Systematic Review"],"prefix":"10.1111","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8435-1208","authenticated-orcid":false,"given":"Wesam Taher","family":"Almagharbeh","sequence":"first","affiliation":[{"name":"Department of Medical and Surgical Nursing, Faculty of Nursing University of Tabuk  Tabuk Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8538-5250","authenticated-orcid":false,"given":"Sameer A.","family":"Alkubati","sequence":"additional","affiliation":[{"name":"Department of Medical Surgical Nursing, College of Nursing University of Ha'il  Ha'il Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0846-8579","authenticated-orcid":false,"given":"Amal Ali","family":"Alasmari","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Nursing, Faculty of Nursing University of Tabuk  Tabuk Saudi 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