Improving HIV preexposure prophylaxis uptake with artificial intelligence and automation: a systematic review

Abstract

Objectives: To identify studies promoting the use of artificial intelligence (AI) or automation with HIV preexposure prophylaxis (PrEP) care and explore ways for AI to be used in PrEP interventions.

Design: Systematic review.

Methods: We searched in the US Centers for Disease Control and Prevention Research Synthesis database through November 2023; PROSPERO (CRD42023458870). We included studies published in English that reported using AI or automation in PrEP interventions. Two reviewers independently reviewed the full text and extracted data by using standard forms. Risk of bias was assessed using either the revised Cochrane risk-of-bias tool for randomized trials for randomized controlled trials or an adapted Newcastle-Ottawa Quality Assessment Scale for nonrandomized studies.

Results: Our search identified 12 intervention studies (i.e., interventions that used AI/automation to improve PrEP care). Currently available intervention studies showed AI/automation interventions were acceptable and feasible in PrEP care while improving PrEP-related outcomes (i.e., knowledge, uptake, adherence, discussion with care providers). These interventions have used AI/automation to reduce workload (e.g., directly observed therapy) and helped non-HIV specialists prescribe PrEP with AI-generated clinical decision-support. Automated tools can also be developed with limited budget and staff experience.

Conclusions: AI and automation have high potential to improve PrEP care. Despite limitations of included studies (e.g., the small sample sizes and lack of rigorous study design), our review suggests that by using aspects of AI and automation appropriately and wisely, these technologies may accelerate PrEP use and reduce HIV infection.

Figure 1:

Flow diagram of included studies