Applications of Artificial Intelligence in Sexually Transmitted Infections: From Symptom Analysis to Image Recognition

This thesis explores how artificial intelligence (AI) can detect sexually transmitted infections (STIs) early by analysing patient symptoms, medical records, and skin photos. The research developed computer programs that identify STIs with high accuracy, discovering that combining clinical information with images works better than using either alone. A study of user preferences revealed people want affordable, accurate AI health apps with doctor-verified results that encourage timely medical care. The prototype Image Capture app is currently being piloted at Melbourne Sexual Health Centre, with a public screening version under development. This work could transform how STIs are detected and managed worldwide.