{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T09:32:54Z","timestamp":1774690374451,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Foundation for Science and Technology (FCT)","doi-asserted-by":"publisher","award":["UIDB\/04279\/2020"],"award-info":[{"award-number":["UIDB\/04279\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nursing Reports"],"abstract":"<jats:p>Background\/Objetives: Pressure injuries pose a significant challenge in healthcare, adversely impacting individuals\u2019 quality of life and healthcare systems, particularly in intensive care units. The effective identification of at-risk individuals is crucial, but traditional scales have limitations, prompting the development of new tools. Artificial intelligence offers a promising approach to identifying and preventing pressure injuries in critical care settings. This review aimed to assess the extent of the literature regarding the use of artificial intelligence technologies in the prediction of pressure injuries in critically ill patients in intensive care units to identify gaps in current knowledge and direct future research. Methods: The review followed the Joanna Briggs Institute\u2019s methodology for scoping reviews, and the study protocol was prospectively registered on the Open Science Framework platform. Results: This review included 14 studies, primarily highlighting the use of machine learning models trained on electronic health records data for predicting pressure injuries. Between 6 and 86 variables were used to train these models. Only two studies reported the clinical deployment of these models, reporting results such as reduced nursing workload, decreased prevalence of hospital-acquired pressure injuries, and decreased intensive care unit length of stay. Conclusions: Artificial intelligence technologies present themselves as a dynamic and innovative approach, with the ability to identify risk factors and predict pressure injuries effectively and promptly. This review synthesizes information about the use of these technologies and guides future directions and motivations.<\/jats:p>","DOI":"10.3390\/nursrep15040126","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T05:03:07Z","timestamp":1744174987000},"page":"126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5809-3788","authenticated-orcid":false,"given":"Jos\u00e9","family":"Alves","sequence":"first","affiliation":[{"name":"Center for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Cat\u00f3lica Portuguesa, 4169-005 Porto, Portugal"},{"name":"Intensive Care Unit, Braga Local Healthcare Unit, 4710-243 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8499-8856","authenticated-orcid":false,"given":"Rita","family":"Azevedo","sequence":"additional","affiliation":[{"name":"Center for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Cat\u00f3lica Portuguesa, 4169-005 Porto, Portugal"},{"name":"Intensive Care Unit, Braga Local Healthcare Unit, 4710-243 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3603-2656","authenticated-orcid":false,"given":"Ana","family":"Marques","sequence":"additional","affiliation":[{"name":"Center for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Cat\u00f3lica Portuguesa, 4169-005 Porto, Portugal"},{"name":"Intensive Care Unit, Gaia and Espinho Local Healthcare Unit, 4434-502 Vila Nova de Gaia, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2666-7391","authenticated-orcid":false,"given":"R\u00faben","family":"Encarna\u00e7\u00e3o","sequence":"additional","affiliation":[{"name":"Center for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Cat\u00f3lica Portuguesa, 4169-005 Porto, Portugal"},{"name":"Cardiology Intensive Care Unit, S\u00e3o Jo\u00e3o Local Healthcare Unit, 4200-319 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6348-3316","authenticated-orcid":false,"given":"Paulo","family":"Alves","sequence":"additional","affiliation":[{"name":"Center for Interdisciplinary Research in Health, Faculty of Health Sciences and Nursing, Universidade Cat\u00f3lica Portuguesa, 4169-005 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref_1","first-page":"38","article-title":"The Pathophysiological Links between Pressure Ulcers and Pain and the Role of the Support Surface in Mitigating Both","volume":"11","author":"Gefen","year":"2020","journal-title":"Wounds Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1097\/NNR.0000000000000258","article-title":"Hospital-Acquired Pressure Injury: Risk-Adjusted Comparisons in an Integrated Healthcare Delivery System","volume":"67","author":"Rondinelli","year":"2018","journal-title":"Nurs. 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