Clinical features-based machine learning models to separate sexually transmitted infections from other skin diagnoses.

Introduction: Many sexual health services are overwhelmed and cannot cater for all the individuals who present with sexually transmitted infections (STIs). Digital health software that separates STIs from non-STIs could improve the efficiency of clinical services. We developed and evaluated a machine learning model that predicts whether patients have an STI based on their clinical features.

Determining the screening frequency for sexually transmitted infections for people who use HIV pre-exposure prophylaxis: a systematic review and meta-analysis.

Objectives: Although the World Health Organization recommends 'frequent' screening of sexually transmitted infections (STI) for people who use pre-exposure prophylaxis for HIV, there is no evidence for optimal frequency.

Methods: We searched five databases and used random-effects meta-analysis to calculate pooled estimates of STI test positivity. We narratively synthesized data on secondary outcomes, including adherence to recommended STI screening frequency and changes in STI epidemiology.