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

Area of Science:

• Medical Technology
• Artificial Intelligence in Healthcare
• Wound Care Innovation

Background:

• Burn and chronic wound care faces challenges with subjective visual assessments, leading to inconsistent evaluations.
• Aging populations and comorbidities like diabetes exacerbate the global burden of complex wounds.
• Conventional methods lack objectivity, reproducibility, and scalability in wound assessment and prognostication.

Purpose of the Study:

• To review the application of artificial intelligence (AI), particularly deep learning and computer vision, in evaluating burns and chronic wounds.
• To synthesize research on AI for wound recognition, segmentation, outcome prediction, and integration into digital health platforms.
• To identify current advancements and persistent challenges in AI-driven wound care.

Main Methods:

• Narrative review of studies published between 2015 and 2025.
• Focus on three key AI domains: image-based wound analysis, predictive modeling, and telemedicine integration.
• Analysis of performance metrics for deep learning models, including segmentation accuracy and classification sensitivity.

Main Results:

• Convolutional neural networks achieve high performance in wound segmentation (Dice >0.85) and classification (sensitivity >0.90).
• Predictive models demonstrate strong accuracy (0.80-0.95 AUC) for healing, infection, and other outcomes.
• AI integration into smartphone apps, telehealth, and smart dressings shows feasibility in early pilots.

Conclusions:

• AI, especially deep learning, provides objective and reproducible wound assessment, surpassing traditional methods.
• Significant potential exists for AI to enhance wound care through improved diagnostics and remote monitoring.
• Addressing algorithmic bias, data diversity, and regulatory hurdles is crucial for safe and equitable AI deployment in wound management.