Advancing Wound Care With AI: From Precision Diagnostics to Predictive Insights

Artificial intelligence (AI) continues to reshape wound care by improving diagnostic precision, automating assessments, and enabling data-driven prognostic insights. Recent developments highlight the expanding role of AI across a spectrum of wound types, including diabetic foot ulcers, pressure injuries, venous leg ulcers, and burns.

Automated Segmentation and Diagnostic Intelligence
Traditional wound assessments often rely on manual measurements, which are limited by variability and subjectivity. Deep learning models, like convolutional neural networks (CNNs), now enable high-accuracy image segmentation for wound boundaries, tissue types, and depth estimation. For example, a ResNet101-based Mask R-CNN model outperformed many conventional techniques in burn wound segmentation. This model combines ResNet101, a deep residual neural network used for extracting image features, with Mask R-CNN, a framework designed to detect and segment objects within images, making it well-suited for identifying wound boundaries with high precision.

Additionally, composite frameworks and mobile-friendly models, like AutoTrace and MobileNetV2-based CNNs, extend this segmentation precision into remote care settings. These tools enable real-time wound analysis on portable devices with accuracies exceeding 90 percent for select characteristics, such as wound area and boundary delineation.  

Following segmentation, AI-powered tissue classification offers increasingly accurate identification of wound tissue types. Tools using deep neural networks and superpixel-based segmentation can identify tissue types (e.g. granulation, necrosis, epithelialization). An example is the use of VGG16-based fully convolutional networks, which have shown high accuracy in classifying chronic wound tissues, with particularly strong performance in distinguishing granulation and slough.

Beyond assessment, AI models can now predict wound healing outcomes using a variety of patient-specific inputs, including wound area, tissue composition, and comorbidities. Gradient-boosted decision trees have achieved AUCs around 0.85 in forecasting chronic wound non-healing, indicating strong performance in distinguishing between healing trajectories. In parallel, mobile applications and wearable systems integrate AI with sensors to monitor healing in real time, capturing biomarkers like skin temperature and pH. These tools support longitudinal tracking and early identification of wound deterioration.

Current Challenges and Future Directions
Despite this rapid progress, clinical adoption faces several hurdles, including data heterogeneity, algorithm generalizability, lack of large and diverse training datasets, and concerns about data privacy. Model transparency, regulatory alignment, and clinician trust remain priorities. Additionally, ensuring that AI tools are user-friendly and integrate smoothly into existing clinical workflows is essential for acceptance and utilization.

References
Liu, H, Sun, W, Cai, W, et al. Current status, challenges, and prospects of artificial intelligence application in wound repair theranostics. Theranostics. 2025;15(5):1662-1688. https://pubmed.ncbi.nlm.nih.gov/39897550/