Beyond the Clinic: Artificial Intelligence Transforming STI Self-Assessment, Early Detection, and Personalized Decision Support

Sexually transmitted infections (STIs) remain a major global public health challenge, while stigma, privacy concerns, and barriers to healthcare access continue to delay diagnosis and treatment. This narrative review summarizes the current evidence on the use of artificial intelligence (AI) to support STI self-assessment and digital sexual healthcare while critically discussing its current applications, challenges, and limitations, based on a PubMed literature search. Existing studies demonstrate the potential of machine-learning algorithms for individualized HIV/STI risk prediction, symptom-based assessment, automated evaluation of genital lesions, and differentiation of sexually transmitted from non-sexually transmitted conditions, with reported diagnostic performance ranging from AUCs of approximately 0.75–0.95 for symptom assessment models up to 0.893 for multimodal image-based lesion classification and validation accuracies of 94.4% for real-world image analysis platforms. Advances in computer vision and radiomics further highlight the ability of image-based AI systems to identify diagnostically relevant lesion characteristics while improving model interpretability. In parallel, generative AI chatbots are increasingly being explored as tools for sexual health education, personalized risk assessment, behavioural support, triage, and linkage to care. These technologies also influence psychological aspects of sexual health by providing private and accessible support, facilitating risk appraisal, and addressing anxiety associated with STI concerns. However, insufficient demographic diversity and the reliance on retrospective training datasets, privacy and data security concerns, limited prospective validation, and uncertainty regarding real-world clinical effectiveness remain important barriers to the widespread adoption of these technologies. To address some of these limitations, future developments are expected to focus on multimodal and explainable AI systems integrated within digital sexual health ecosystems, with clinicians remaining central to the validation, interpretation, and contextualization of AI-generated assessments and the delivery of patient-centred care.