Artificial Intelligence for Wound Healing (I.E., Real-Time Monitoring, Image-Based, Bioinformatics, and Precision Regulation)

Le résumé fourni par la source

Evaluating wound healing plays a pivotal role in ensuring effective wound care. It is imperative to adhere to clinical protocols, which involve regularly documenting aspects such as wound size and tissue composition, as they are fundamental for proper wound management. Manual measurements are time-consuming, expensive, and difficult to repeat. Efficient evaluation of acute and chronic wounds can improve diagnosis, treatment plans, workload reduction, and overall health-related quality of life. Although AI has numerous applications in health sciences, clinical and computational developments are still needed for excellent wound care. Artificial Intelligence has the ability to create a completely automated wound measuring instrument that can measure various wound aspects accurately and be utilized for treatment planning and wound evaluation. However, only wound datasets with a limited number of photos are now publicly available, which makes it difficult to construct an AI-assisted wound evaluation tool without a significant number of high-quality data points. By gathering more picture data, we can further enhance our methodology and improve the deep learning models. Making the gathering of study information at wound clinics a regular practice might help achieve this. AI-assisted devices may be especially helpful in isolated locations with limited access to wound care services. Hence, the AI-assisted wound healing approach should be to create a dependable, easy-to-use tool that will help doctors provide their patients with the best possible wound care.

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