- Review
21 Pages
Background/Objectives: Sexually transmitted infections (STIs) continue to pose a global public health problem, exacerbated by barriers to access, stigma and the limitations of traditional screening models. Artificial intelligence (AI) has emerged as a tool with the potential to enhance early detection, risk stratification and personalised counselling. This article critically analyses the current role of AI in STI prevention (encompassing primary and secondary prevention, including early detection), identifying validated applications, limitations and ethical implications. Methods: A narrative review was conducted of the literature published between 2020 and 2026, using biomedical databases, with a focus on original studies, systematic reviews and documents from leading organisations on the applications of machine learning, computer vision and conversational agents in the prevention, diagnosis and surveillance of STIs, including HIV. Results: Machine learning algorithms applied to clinical and behavioural data stratify the individual risk of STIs with satisfactory performance, supporting the recommendation for pre-exposure prophylaxis. Computer vision systems for the early detection of STIs identify skin and genital lesions with reasonable accuracy, although generalisation to diverse populations remains limited. Chatbots have proven to be acceptable and effective in promoting HIV testing and adherence to PrEP, particularly among key populations. Challenges remain regarding algorithmic bias, privacy and the lack of established regulatory frameworks. Conclusions: AI is a promising complementary tool, rather than a substitute for conventional STI prevention strategies, and its implementation must be accompanied by rigorous clinical validation, human oversight and a deliberate focus on health equity.
Venereology
7 October 2026



