Advancing burn wound healing with artificial intelligence (AI): Innovative solutions in medical technology

Abstract

Assessment of burn injuries, determining wound depth and total body surface area (TBSA), is a critical but also subjective and inaccurate process. These diagnostic errors can lead to unfortunate patient outcomes and the useless distribution of healthcare resources. Artificial intelligence (AI) has emerged as a promising technology to improve precision in burn care. Our review analyzes the current applications, foundational technologies, and significant challenges of AI in advancing burn wound healing. This review is based on a regulated analysis of key scientific literature. It also covers the range of AI applications, from initial diagnosis and assessment to final treatment. It examines the primary computational models and datasets that enable these novelties, as well as the practical and ethical barriers to their clinical implementation. The review reveals that deep learning (DL) models have significant potential to improve the precision of burn wound diagnosis. It also shows that AI is particularly effective at segmenting wound boundaries, grading burn depth, and approximating %TBSA. This review highlights AI's increasing role in predicting healing paths and personalizing treatment plans. The analysis identifies various barriers to widespread implementation, including the lack of diverse datasets, the risk of algorithmic bias, and the "black box" nature of some models, which can inhibit clinical trust. AI holds considerable potential to enhance the precision and objectivity of burn care by serving as a powerful decision support tool for clinicians. Realizing the full potential of these innovative solutions requires a mutual effort to address the known research gaps.