An Integrated Deep Learning and LLM Model for Burn Wound Depth Recognition

Accurate burn depth assessment remains a challenge, especially in emergency settings. This study aimed to develop a low-cost AI-based system for burn wound classification using deep learning and large language models (LLMs). A total of 397 burn wound images from public databases were augmented to 7156 images and categorized by depth. A classification model was trained using PaddlePaddle, and a burn-specific LLM was developed based on clinical guidelines. Model performance was evaluated using accuracy, recall, and F1 score and compared against 10 medical students and six general LLMs on 80 out-of-sample images. Our model achieved an overall accuracy of 96.82% and F1 score of 96.70%, outperforming medical students (F1:76.63%) and general LLMs (F1:68.75–73.75%). In a separate test using ten guideline-based true/false questions, all AI models answered correctly, whereas students had only 64% accuracy. This integrated model offers accurate burn depth recognition and guideline-based treatment suggestions, addressing the shortage of burn care specialists, and supporting medical education.