WOUNDWATCH: A Mobile Monitoring System to Enhance Diabetic Wound Care through AI-Assisted Risk Detection and Remote Medical Support at Tupi, South Cotabato

The presented study is about WoundWatch, an AI-based mobile monitoring system that aims to transform diabetic wound care by the means of infection risk detection at the earliest stage, automatic care reminders, and remote medical consultation. The system employs advanced image analysis and a friendly mobile interface that allows patients to frequently check the wound status, get prompt alerts, and communicate with healthcare providers for immediate guidance. Wound Watch is equipped with features for patients having different levels of digital literacy, empowering them with self-management and better compliance with wound care protocols. The usability test involving 100 diabetic patients showed that the system was very easy to use (around 84%), users were highly involved, and they expressed their satisfaction with the system’s convenient functionalities and remote support. Some users pointed out that they experienced problems with system responsiveness and navigation of advanced features, thus, these are the areas where development is planned. The main findings demonstrate that Wound Watch is instrumental in building patient confidence, facilitating early interventions, and leading to better wound care results. This study serves as a bridge between AI technologies and human-centered design with the aim to improve chronic disease management in low-resource settings, thus, it has substantial potential for helping patients manage wounds better.