Impact of Artificial Intelligence on Wound Assessment and Healing

The biological process of wound healing is dynamic and complex, requiring the interaction of several cells and molecular pathways in order to repair tissues. Conventional wound evaluation and treatment methods are largely dependent upon subjective clinical evaluation which can lead to inconsistency and slow healing process. Artificial Intelligence (AI), which discusses to the technologies of Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), and Computer Vision (CV), has become one of the influential tools in wound care of the present day. AI improves the precision of diagnosis and treatment planning by enabling automatic wound detection, segmentation, classification, and precise measurement of the wound size and depth. Convolutional neural networks (CNNs) are AI-driven systems that have demonstrated superior performance in predicting healing patterns, infection risk, and treatment results in cases of diabetic foot ulcers (DFU), burns, venous ulcers, and pressure ulcers. In addition, AI-monitored devices and smart wearings also offer real-time wound monitoring to improve patient adherence and clinical decision-making. Individualized wound care AI is accelerating personalized wound care, drug discovery, and the creation of intelligent biomaterials through the integration of AI with advanced imaging, bioinformatics, and 3D bioprinting technologies. This review examines the existing applications, clinical benefits, and future potential of the use of AI in wound assessment and healing, where it is suggested that AI will enhance accuracy, efficiency, and patient-centered wound management.