How AI-Powered Hydrogel Dressings are Revolutionizing Chronic Wound Care

An emerging generation of smart wound dressings that combine conductive hydrogel technology with artificial intelligence (AI) is showing promise in the management of refractory wounds—a category that includes pressure ulcers, diabetic ulcers, and joint-related injuries. These chronic wounds are notoriously resistant to healing, often leading to prolonged disability, infections, and increased healthcare costs.

Published in Nano-Micro Letters, a review by She et al. (2025) presents an in-depth exploration of electroactive hydrogel dressings integrated with real-time biosensors and AI-driven analytics, offering a dual function: continuous wound monitoring and targeted therapeutic delivery.

Unlike traditional hydrogels that simply maintain moisture and absorb exudate, conductive hydrogels are embedded with materials such as carbon nanotubes, graphene derivatives, MXenes, and conductive polymers. These materials not only facilitate electrical conductivity but also interact dynamically with the wound microenvironment—monitoring critical parameters like pH, temperature, glucose, and pressure.

Through integrated sensors, these hydrogel platforms convert biochemical changes into electrical signals that can be transmitted via wireless modules to healthcare providers. This capability allows for non-invasive, remote tracking of healing progression, which is particularly valuable for patients requiring long-term care or those at home.

AI is central to this innovation. Machine learning models—including K-nearest neighbors (KNN), artificial neural networks (ANN), and convolutional neural networks (CNN)—are being used to interpret physiological signals collected from the sensors. These models can predict wound healing stages, detect early signs of infection, and guide personalized treatment decisions. In one case, a CNN model trained on 2,400 pH-sensing images achieved over 94% accuracy in identifying wound infection status.

Importantly, this technology isn’t just theoretical. Several studies highlighted in the review demonstrate in vivo applications, including temperature-sensitive dressings that adjust treatment based on early fever detection, and hydrogels capable of glucose monitoring in diabetic foot ulcers using colorimetric or resistance-based signal outputs.

The clinical use cases are as varied as they are impactful. For pressure ulcers, sensors embedded within the dressing monitor pressure points and help guide repositioning strategies. For diabetic ulcers, real-time glucose and pH monitoring informs both systemic glycemic control and localized antimicrobial treatment. And for articular wounds, wearable hydrogel sensors conform to joint movement while detecting strain, preventing re-injury during rehabilitation.

Yet despite this progress, the authors caution that clinical translation remains limited. Current systems often monitor only one or two biomarkers, missing the multidimensional complexity of chronic wounds. The integration of multimodal sensing, advanced data fusion, and cross-modal AI algorithms will be crucial for broader adoption.

Future directions call for expanding the range of treatable wounds—beyond pressure, diabetic, and articular types—to include burns, ischemic ulcers, and oncology-associated wounds. Moreover, commercialization remains a hurdle due to production costs and data security concerns associated with wireless transmission.

Still, the trajectory is clear. Smart hydrogel dressings, empowered by AI, represent a significant leap toward precision wound care—combining diagnostics, therapeutics, and real-time analytics in a single platform. As these technologies evolve, they could reshape how chronic wounds are managed both in clinical settings and at home.