A Multimodal Artificial Intelligence Reasoning Framework for Burn Diagnosis.

INTRODUCTION: This work presents an AI reasoning framework for burn depth diagnosis that emphasizes transparency and interpretability. The system addresses a critical need in burn care for consistent, explainable assessments, particularly in remote and resource-limited environments where access to burn specialists is limited. MATERIALS AND METHODS: The proposed framework employs a multimodal structural reasoning mechanism that integrates digital photographs with ultrasound imaging, including B-mode and Tissue Doppler Imaging (TDI). These complementary modalities capture both surface features and subsurface tissue dynamics. A chain-of-thought reasoning process links visual and acoustic cues to clinically meaningful indicators of burn severity, providing step-by-step explanations that mirror human diagnostic logic. RESULTS: The multimodal reasoning framework demonstrates high diagnostic accuracy in classifying burn depth across three clinically relevant categories. The structured reasoning output provides interpretable explanations for each diagnostic decision, enabling medics to validate the model's conclusions. Experimental findings show that the system demonstrates strong performance relative to retrospective human evaluation. CONCLUSIONS: This multimodal AI reasoning framework offers a practical, deployable solution for modern burn assessment. Its transparent, step-wise reasoning enhances clinical trust, improves decision confidence, and supports care delivery in settings lacking expert supervision. The approach establishes a foundation for explainable, high-performance AI in both military and civilian burn care.