AI-Driven Oral Disease Diagnosis: A Review of the SwaLife Image-Based Model Training and Detection Platform

Abstract

Oral diseases represent a significant global health burden, affecting millions and contributing to substantial morbidity and mortality when diagnosis is delayed. While conventional diagnostic approaches rely on clinical expertise and histopathological confirmation, they remain subject to inter-observer variability, subjectivity, and limited accessibility in resource-constrained settings. The emergence of artificial intelligence (AI) and deep learning technologies offers transformative potential for oral disease detection. This review examines the SwaLife Oral Disease Diagnosis platform, a userfriendly, web-based tool that democratizes AI-driven diagnostics by enabling clinicians, researchers, and educators to train customized machine learning models using their own datasets of healthy and diseased oral images. We describe the platform's architecture, workflow, and capabilities, highlighting its disease-agnostic design, intuitive interface, and capacity for rapid deployment. Through a case study of oral cancer detection, we demonstrate the platform's diagnostic performance and clinical utility. Comparative analysis with existing AI-based oral diagnostic systems reveals SwaLife's unique strengths in user-controlled model training, customizability, and accessibility. We discuss applications ranging from clinical screening and early detection to dental education and telemedicine, while addressing limitations and regulatory considerations. Future directions include integration of 3D imaging, automated lesion segmentation, and federated learning frameworks. The SwaLife platform represents a pragmatic bridge between advanced AI innovation and practical clinical implementation, contributing to the democratization of intelligent oral disease diagnostics in diverse healthcare settings.