Artificial Intelligence in Burn and Wound Care: Image Analysis, Prediction, and Clinical Integration

Burn injuries and chronic wounds impose a substantial and growing global health and economic burden, particularly in low- and middle-income countries and among aging populations with diabetes, vascular disease, and immobility. Conventional wound assessment depends heavily on visual inspection, manual measurements, and clinician experience, leading to variability in burn-depth estimation, wound sizing, and prognostication. Artificial intelligence, especially deep learning-based computer vision, has emerged as a promising approach to provide objective, reproducible, and scalable evaluation of burns and complex wounds. In this narrative review, we synthesize studies published between 2015 and 2025 focused on three domains: image-based wound recognition and segmentation, predictive modeling of outcomes such as healing, graft success, infection, and amputation, and integration of artificial intelligence into telemedicine platforms and smart technologies for remote monitoring. Across multiple datasets, convolutional neural networks achieve segmentation Dice coefficients frequently exceeding 0.85 and burn-depth or tissue-type classification sensitivities above 0.90, while multimodal prediction models reach accuracies and areas under the receiver operating characteristic curve of approximately 0.80-0.95. Early clinical pilots demonstrate the feasibility of embedding artificial intelligence tools into smartphone applications, telehealth workflows, and sensor-enabled dressings. Nonetheless, persistent challenges related to algorithmic bias across skin tones, limited dataset diversity, opaque model behavior, workflow integration, and evolving regulatory frameworks must be addressed before artificial intelligence-enabled wound care systems can be safely and equitably deployed at scale.