Artificial Intelligence and Algorithmic Bias in Burn Care: A Literature Review of Ethical Challenges.

Abstract

Artificial intelligence (AI) is increasingly integrated into burn care for triage, burn-depth assessment, prognostic scoring, pain management, and telemedicine-enabled resource allocation. These tools promise greater efficiency and precision, yet raise substantial ethical concerns regarding transparency, accountability, and bias. This narrative review synthesizes literature from 2010 to 2025 on AI, predictive modeling, and digital tools in burn care, supplemented by evidence from critical care, emergency medicine, radiology, dermatology, and oncology. Systematic searches of PubMed, Embase, and Scopus identified studies that reported algorithm development or deployment and discussed or allowed inference about equity, fairness, or interpretability. Across medical domains, algorithms frequently misclassified outcomes for racial and ethnic minorities, socioeconomically disadvantaged patients, women, older adults, and individuals with complex comorbidities, while burn-specific models rarely evaluated subgroup performance or reported demographic composition. Common problems included unrepresentative datasets, opaque modeling pipelines, and the absence of formal bias audits. Ethical analyses were fragmented and seldom grounded in established frameworks of biomedical ethics or AI governance. This review argues that current trajectories risk embedding and amplifying inequities in an already vulnerable burn population. It proposes concrete strategies for fairness-oriented design, reporting, validation, and post-deployment monitoring, emphasizing justice, nonmaleficence, transparency, accountability, and stakeholder engagement. Responsible adoption of AI in burn care will require moving beyond technical performance alone toward explicit attention to equity and ethical safeguards throughout the model life cycle.