Engineers at the University of California, Santa Cruz, have developed “a-Heal”, an AI wearable device that uses a miniature camera, bioelectronics, and reinforcement learning to diagnose a wound’s healing stage and delivery personalized AI recovery treatments.
In pre-clinical trials, the closed-loop system reduced healing time by 25% through continuous wound imaging, analyzing its own progress with an “AI physician,” for real-time therapy adaptation to optimize each recovery biological phase.
Electromagnetic healing devices like “a-Heal” solve the issue of wound scarring through precision of AI healing with real time feedback and automated treatment. AI recovery technology is beneficial for patients with limited access to hospitals or clinics.
Smarter Healing Tools
The research published in NPJ biomedical innovations was done by Professor Marco Rolandi and others at UC Davis with him at UC Santa Cruz. The scientists developed a system that integrates a camera, bioelectronic devices, and AI into what they term a “closed-loop system.”
“Our system takes all the cues from the body, and with external interventions, it optimizes the healing progress,” Rolandi said.
The camera takes pictures of the stages of wound healing images of the wound every two hours. These healthy or images of infected wounds are analyzed by a machine learning model, named the “AI physician.” The machine designed by Professor Marcella Gomez determines the stages of the wound and whether treatment should be started.
“It’s essentially a microscope in a bandage, individual images say little, but over time, continuous imaging lets AI spot trends, wound healing stages, flag issues, and suggest treatments,” said Associate Professor Mircea Teodorescu.
According to the condition diagnosis, a-Heal treats fluoxetine wound healing for inflammation management, or an electric field to direct tissue healing, a mechanism referred to as electric field cell migration.
Learning Through AI
a-Heal’s core is based on reinforcement learning, a form of AI in wound care that improved through trial and error.
The system learns the process of wound healing and uses an adapted alternative treatments design to optimize the amount of medicine or level of electric stimulation.
“It’s not enough to just have the image, you need to process that and put it into context. Then, you can apply the feedback control,” Gomez said.
The good news is that preclinical trial was promising. Wounds treated with a-Heal healed approximately 25% faster than conventionally treated wounds. Doctors even get to review the data using remote patient monitoring applications, ensuring safe monitoring and timely intervention.
Moreover, researchers believe that these biomedical engineering innovations will change and alter wound care, not only accelerating AI recovery in wounds but also reviving non-progressive healing in chronic wounds.
With continuous wound healing stages images and treatment suggestions, the technology shows how medicine and AI can work together. As the research carries on, a-Heal’s dream lies in its ability to offer AI recovery that is personalized, accessible, and deeply human-focused.
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