Table 2_Artificial intelligence in perioperative pressure injury management: a scoping review of evidence, gaps, and implications for population health and digital public health policy.docx

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 hours of surgery. Evidence for postoperative early warning systems is absent.</p>Conclusion<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.</p>