AI-Enhanced Dermatological Diagnostic Tool for Global Skin Health Improvement

There is a need for new and accessible diagnostic solutions globally, as the prevalence of skin disorders increases. This thesis presents an AI-Enhanced Dermatological Diagnostic Tool which utilizes state of the art machine learning techniques to augment the powers of the dermatologists in detecting and classifying skin conditions. Our system uses convolutional neural networks (CNN) trained on various dermatological datasets and is able to efficiently identify diseases including melanoma, psoriasis, eczema and benign lesions with high accuracy. The proposed tool has the capability to integrate daily demographic data and patient history to enhance the diagnostic precision and at the same time have a user friendly interface to allow easy access via mobile and telemedicine platform. It is designed for real time operation, to fill healthcare voids in the under served areas with rapid, cheap, reliable diagnostic aide. The continuous learning algorithms guarantee the response’s adaptability to new data, consequently creating a dynamic diagnostic response that changes over time. Pilot trials demonstrate the potential to reduce diagnostic delays, improve adherence to follow-up care and empower patients for early stage detection and for preventive care. The work underscore how artificial intelligence will change dermatological diagnostics to help improve global skin health.