Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic

Abstract

Purpose of review: We review applications of artificial intelligence (AI), including machine learning (ML), in the field of HIV prevention.

Recent findings: ML approaches have been used to identify potential candidates for preexposure prophylaxis (PrEP) in healthcare settings in the USA and Denmark and in a population-based research setting in Eastern Africa. Although still in the proof-of-concept stage, other applications include ML with smartphone-collected and social media data to promote real-time HIV risk reduction, virtual reality tools to facilitate HIV serodisclosure, and chatbots for HIV education. ML has also been used for causal inference in HIV prevention studies. ML has strong potential to improve delivery of PrEP, with this approach moving from development to implementation. Development and evaluation of AI and ML strategies for HIV prevention may benefit from an implementation science approach, including qualitative assessments with end users, and should be developed and evaluated with attention to equity.

Keywords: Artificial intelligence; Big data; Data science; Human immunodeficiency virus (HIV); Machine learning; Prevention.

Conflict of interest statement

Conflict of interest: Julia Marcus has consulted for Kaiser Permanente Northern California on a research grant from Gilead Sciences. Douglas Krakower has conducted research with project support from Gilead Sciences; has received honoraria for authoring or presenting continuing medical education content for Medscape, MED-IQ, and DKBMed; and has received royalties for authoring content for Uptodate, Inc. Laura Balzer and Whitney Sewell declare no conflicts.