Abstract
OBJECTIVES: To explore how artificial intelligence (AI) can enhance infodemiology, which distributes and scans information in the electronic medium, to process social media posts for HIV preexposure prophylaxis (PrEP). DESIGN: Systematic review. METHODS: We searched in the U.S. Centers for Disease Control and Prevention's Prevention Research Synthesis database through June 2024 (PROSPERO: CRD42023458870). We included infodemiology studies published in English and reported using AI to process social media posts on PrEP. Two reviewers independently screened citations, extracted data, and conducted a risk of bias assessment using the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies. Findings are narratively summarized. RESULTS: Of the 135 citations screened, eight infodemiology studies were identified, analyzing over 58.9 million posts. Infodemiology studies found the PrEP topics commonly discussed in communities (e.g., barriers of uptake), rumors that may raise public health concerns (e.g., PrEP is a prevention method against COVID-19 infection), geographic locations where concerns regarding risk of acquiring HIV were raised (e.g., most HIV-related posts were from the 10 states with the highest numbers of new HIV diagnoses), and predicted HIV trends (e.g., HIV-related tweets were negatively correlated with the county-level HIV incidence rate in the following year). CONCLUSIONS: Despite the limitations of this review including a small number of studies reviewed, our review suggests social media posts may provide information on real-time PrEP-related concerns, and AI can accelerate and enhance the processing of mass data to identify the information that communities need and the areas/locations that may need HIV prevention intervention.