Artificial intelligence for outcome prediction in burn care: A scoping review.

Abstract

Machine learning (ML) is increasingly applied across burn care, but its clinical utility remains unclear. This scoping review mapped current evidence on ML models developed to predict key outcomes in burn patients, including mortality, sepsis, inhalation injury, delirium, surgical decision making, intraoperative blood loss and acute kidney injury (AKI). Following the Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, two databases were searched from 2020 to 2025. Studies using ML to predict diagnostic, prognostic, or treatment-related outcomes in burn patients were included. Twenty-three studies met inclusion criteria. Mortality prediction was the most advanced domain, with ML models consistently outperforming traditional scores using a small set of routinely collected predictors (age, total body surface area burned, depth, inhalation injury). Sepsis prediction models demonstrated superior accuracy to American Burn Association (ABA) sepsis criteria and Sepsis-3 definitions, particularly when based on focused feature sets. Length of stay models showed modest gains, whilst inhalation injury grading and tracheostomy prediction achieved strong performance. Delirium and intraoperative blood loss prediction models showed moderate to high accuracy, while imaging-based approaches for surgical candidacy produced variable results. AKI prediction using neutrophil gelatinase-associated lipocalin based models achieved area under the receiver operating characteristic curve (AUROC) values of up to 0.97. ML shows substantial promise for predicting key outcomes in burn care, particularly mortality, inhalation injury and AKI. However, most studies were retrospective, single-centre, and lacked external validation, highlighting the need for robust multicentre and prospective evaluation before clinical deployment.