{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:01:25Z","timestamp":1790834485402,"version":"4.1.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Perioperative pressure injury (POPI) is a significant global public health challenge, contributing to substantial iatrogenic morbidity and heavy economic burden worldwide. Traditional prevention is limited by insufficient assessment tools, the \u201cblack box\u201d nature of intraoperative monitoring, and delayed intervention. Artificial intelligence (AI) offers a novel approach by integrating multidimensional data for real-time risk prediction; however, no scoping review has systematically mapped AI applications across the full perioperative continuum from a population health perspective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Systematic searches were conducted in PubMed, Embase, CINAHL, Cochrane Library, IEEE Xplore, ACM Digital Library, Web of Science, and Scopus from inception to December 11, 2025. Studies applying machine learning, conventional regression-based benchmarking tools, or intelligent sensor-based monitoring for POPI prediction, monitoring, or prevention during the perioperative period were included. Two reviewers independently performed screening and data extraction, followed by descriptive synthesis and evidence mapping.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Six studies (2018\u20132025), all from East Asia, were included. This geographic concentration highlights an unassessed generalizability and algorithmic fairness risk, rather than an established inequity finding. Evidence concentrated on post-hoc risk stratification (five prediction studies), predominantly using machine learning models (e.g., Random Forest, XGBoost) with AUCs of the best-performing models ranging from 0.806 to 0.836; one study reported a prediction accuracy of 0.9733, which is not directly comparable to AUC. Models identified specialty-specific risk factors (e.g., cardiopulmonary bypass time, forced positioning). Only one proof-of-concept study addressed intraoperative real-time monitoring, visualizing pressure increases after 2\u202fhours of surgery. Evidence for postoperative early warning systems is absent.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Current studies demonstrate the feasibility of specialty-specific post-hoc risk stratification using intraoperative covariates, though truly preoperative models remain absent. Intraoperative monitoring shows early promise, but postoperative early warning is a critical gap, and evidence lacks global diversity. Future research should develop closed-loop intelligent systems integrating preoperative prediction, intraoperative multimodal monitoring, and postoperative warning. Nursing protocols should consider incorporating AI-identified risk factors as hypothesis-generating inputs pending prospective validation, while policymakers should establish frameworks for continuous validation and cross-population calibration to ensure equitable impact.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1952864","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:41:29Z","timestamp":1790833289000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in perioperative pressure injury management: a scoping review of evidence, gaps, and implications for population health and digital public health policy"],"prefix":"10.3389","volume":"14","author":[{"given":"Qin","family":"Zeng","sequence":"first","affiliation":[{"name":"Department of Pediatric Gastroenterology Nursing, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education","place":["Chengdu, China"]},{"name":"Department of Pediatric Outpatient Nursing, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacheng","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Nursing, West China School of Nursing, Sichuan University","place":["Chengdu, Sichuan Province, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education","place":["Chengdu, China"]},{"name":"Department of Pediatric Intensive Care Unit Nursing, West China Second University Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Department of Pediatric Intensive Care Unit Nursing, WCSUH-Tianfu\u00b7Sichuan Provincial Children\u2019s Hospital","place":["Meishan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1089\/wound.2024.0238","article-title":"Prediction models for intraoperative acquired pressure injury of adults: a systematic review and critical appraisal","volume":"15","author":"Xu","year":"2026","journal-title":"Adv Wound Care (New Rochelle)"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1186\/s13054-025-05531-3","article-title":"Effect of blood pressure threshold on adverse outcomes in patients with acute spinal cord injury: a systematic review and meta-analysis","volume":"29","author":"Tanvir","year":"2025","journal-title":"Crit Care"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s13054-024-04874-7","article-title":"Effect of a self-developed fixation device on preventing endotracheal intubation-related pressure injury: a randomised controlled trial","volume":"28","author":"Zhang","year":"2024","journal-title":"Crit Care"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"107754","DOI":"10.1016\/j.compbiomed.2023.107754","article-title":"An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations","volume":"168","author":"Ho","year":"2024","journal-title":"Comput Biol Med"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"104191","DOI":"10.1016\/j.ijnurstu.2022.104191","article-title":"Pressure injuries in Australian public hospitals: a cost of illness study","volume":"130","author":"Nghiem","year":"2022","journal-title":"Int J Nurs Stud"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"114528","DOI":"10.1016\/j.bios.2022.114528","article-title":"A pressure-resistant zwitterionic skin sensor for domestic real-time monitoring and pro-healing of pressure injury","volume":"214","author":"Liu","year":"2022","journal-title":"Biosens Bioelectron"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"173","DOI":"10.5582\/bst.2025.01013","article-title":"Investigating perioperative pressure injuries and factors influencing them with imbalanced samples using a synthetic minority over-sampling technique","volume":"19","author":"Zhou","year":"2025","journal-title":"Biosci Trends"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1038\/s41467-025-67146-0","article-title":"Large-area and soft magnetoelastic sensing for normal and shear stress distribution toward pressure injury prevention","volume":"17","author":"Liu","year":"2025","journal-title":"Nat Commun"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"102560","DOI":"10.1016\/j.artmed.2023.102560","article-title":"Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: a comprehensive review","volume":"141","author":"Toffaha","year":"2023","journal-title":"Artif Intell Med"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1177\/21621918251388015","article-title":"Artificial intelligence in pressure injury diagnosis: a critical appraisal for clinical practice","author":"Wei","year":"2025","journal-title":"Adv Wound Care (New Rochelle)"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10916-024-02038-2","article-title":"Artificial intelligence in operating room management","volume":"48","author":"Bellini","year":"2024","journal-title":"J Med Syst"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.bja.2025.04.004","article-title":"Generative AI models: the next anaesthetic agent?","volume":"135","author":"Julius","year":"2025","journal-title":"Br J Anaesth"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1097\/ALN.0000000000004764","article-title":"Prediction of complications and prognostication in perioperative medicine: a systematic review and PROBAST assessment of machine learning tools","volume":"140","author":"Arina","year":"2024","journal-title":"Anesthesiology"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1186\/s12967-024-05481-4","article-title":"Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis","volume":"22","author":"Mohammadi","year":"2024","journal-title":"J Transl Med"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1186\/s12874-018-0611-x","article-title":"Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach","volume":"18","author":"Munn","year":"2018","journal-title":"BMC Med Res Methodol"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"111343","DOI":"10.1016\/j.jclinepi.2024.111343","article-title":"Large scoping reviews: managing volume and potential chaos in a pool of evidence sources","volume":"170","author":"Alexander","year":"2024","journal-title":"J Clin Epidemiol"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"248","DOI":"10.2152\/jmi.63.248","article-title":"Predictability of pressure ulcers based on operation duration, transfer activity, and body mass index through the use of an alternating decision tree","volume":"63","author":"Setoguchi","year":"2016","journal-title":"J Med Invest Abbreviation"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"e70016","DOI":"10.1002\/rcs.70016","article-title":"Risk factors and prediction model for pressure injuries in patients undergoing Da vinci surgery: stress injury in Da vinci surgery patients","volume":"20","author":"Shen","year":"2024","journal-title":"Int J Med Robot Comput Assist Surg"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"4619","DOI":"10.3390\/s20164619","article-title":"Flexible textile-based pressure sensing system applied in the operating room for pressure injury monitoring of cardiac operation patients","volume":"20","author":"Shih","year":"2020","journal-title":"Sensors (Basel)"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1097\/won.0000000000000388","article-title":"Artificial neural network: a method for prediction of surgery-related pressure injury in cardiovascular surgical patients","volume":"45","author":"Chen","year":"2018","journal-title":"J Wound Ostomy Continence Nurs"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"e135","DOI":"10.1097\/jnr.0000000000000411","article-title":"Predicting the development of surgery-related pressure injury using a machine learning algorithm model","volume":"29","author":"Cai","year":"2020","journal-title":"J Nurs Res"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"143","DOI":"10.12968\/jowc.2024.33.2.143","article-title":"Pressure injury risk factors in adult orthopaedic surgical patients: a cross-sectional study and random forest","volume":"33","author":"Zhao","year":"2024","journal-title":"J Wound Care"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"e101532","DOI":"10.1136\/bmjhci-2025-101532","article-title":"Comparative performance of logistic regression, multilayer perceptron and decision tree models for predicting surgical pressure injuries: a retrospective cohort study","volume":"32","author":"Li","year":"2025","journal-title":"BMJ Health Care Inform"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"113443","DOI":"10.1016\/j.bios.2021.113443","article-title":"Recent advances in wearable biosensing gloves and sensory feedback biosystems for enhancing rehabilitation, prostheses, healthcare, and virtual reality","volume":"190","author":"Demolder","year":"2021","journal-title":"Biosens Bioelectron"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s11154-025-09999-w","article-title":"Use of thermal imaging in the detection of the diabetic foot- review","volume":"27","author":"Mrowiec","year":"2025","journal-title":"Rev Endocr Metab Disord"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"48438","DOI":"10.1021\/acsami.2c13984","article-title":"Biomimetic patch with wicking-breathable and multi-mechanism adhesion for bioelectrical signal monitoring","volume":"14","author":"Zhang","year":"2022","journal-title":"ACS Appl Mater Interfaces"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"105127","DOI":"10.1016\/j.ijnurstu.2025.105127","article-title":"Risk factors for pressure injuries in critical care patients: an updated systematic review","volume":"169","author":"Alderden","year":"2025","journal-title":"Int J Nurs Stud."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"103976","DOI":"10.1016\/j.ijnurstu.2021.103976","article-title":"Repositioning for pressure injury prevention in adults: an abridged Cochrane systematic review and meta-analysis","volume":"120","author":"Gillespie","year":"2021","journal-title":"Int J Nurs Stud"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"e73846","DOI":"10.2196\/73846","article-title":"AI-driven real-time monitoring of cardiovascular conditions with wearable devices: scoping review","volume":"13","author":"Abedi","year":"2025","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"105559","DOI":"10.1016\/j.compbiomed.2022.105559","article-title":"Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients","volume":"147","author":"Kristinsson","year":"2022","journal-title":"Comput Biol Med"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1016\/j.bja.2021.10.052","article-title":"Intraoperative prediction of postanaesthesia care unit hypotension","volume":"128","author":"Palla","year":"2022","journal-title":"Br J Anaesth"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"3859","DOI":"10.1097\/js9.0000000000002354","article-title":"Predicting high-risk factors for postoperative inadequate analgesia and adverse reactions in cesarean delivery surgery: a prospective study","volume":"111","author":"Zhang","year":"2025","journal-title":"Int J Surg"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"e51409","DOI":"10.2196\/51409","article-title":"Longitudinal model shifts of machine learning-based clinical risk prediction models: evaluation study of multiple use cases across different hospitals","volume":"26","author":"Cabanillas Silva","year":"2024","journal-title":"J Med Internet Res"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s13054-024-04860-z","article-title":"Use of artificial intelligence in critical care: opportunities and obstacles","volume":"28","author":"Pinsky","year":"2024","journal-title":"Crit Care"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1038\/s41591-024-02970-3","article-title":"Artificial intelligence in surgery","volume":"30","author":"Varghese","year":"2024","journal-title":"Nat Med"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.3390\/diagnostics13172739","article-title":"Systematic review for risks of pressure injury and prediction models using machine learning algorithms","volume":"13","author":"Barghouthi","year":"2023","journal-title":"Diagnostics (Basel)"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1952864\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:41:30Z","timestamp":1790833290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1952864\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,1]]},"references-count":36,"alternative-id":["10.3389\/fpubh.2026.1952864"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2026.1952864","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,1]]},"article-number":"1952864"}}