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A Multimodal Artificial Intelligence Reasoning Framework for Burn Diagnosis
Md Masudur Rahman1, Mohamed El Masry2,3, Gayle Gordillo2,4
1School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, United States.
Military Medicine
|August 6, 2026
Summary
This AI framework uses multimodal imaging and chain-of-thought reasoning for accurate burn depth diagnosis. It provides transparent, explainable assessments, improving care in remote settings.
Area of Science:
- Artificial Intelligence in Medicine
- Medical Imaging Analysis
- Clinical Decision Support Systems
Background:
- Addresses the need for consistent, explainable burn depth assessments.
- Highlights limitations in expert access, especially in remote or resource-limited areas.
- Focuses on transparency and interpretability in AI for critical care.
Purpose of the Study:
- To develop an AI reasoning framework for transparent and interpretable burn depth diagnosis.
- To integrate multimodal imaging for comprehensive burn assessment.
- To provide explainable AI solutions for burn care.
Main Methods:
- Employs a multimodal structural reasoning mechanism integrating digital photographs with ultrasound imaging (B-mode and Tissue Doppler Imaging).
- Utilizes a chain-of-thought reasoning process to link visual and acoustic cues to burn severity.
- Mimics human diagnostic logic for step-by-step explanations.
Main Results:
- Achieved high diagnostic accuracy in classifying burn depth into three clinically relevant categories.
- Generated interpretable explanations for each diagnostic decision, allowing for validation.
- Demonstrated strong performance compared to retrospective human evaluation.
Conclusions:
- Presents a practical, deployable multimodal AI reasoning framework for burn assessment.
- Enhances clinical trust and decision confidence through transparent, step-wise reasoning.
- Establishes a foundation for explainable AI in military and civilian burn care.
