Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness

TL;DR

This study presents a clinical trial demonstrating that an AI-optimized social network intervention significantly reduces HIV risk behaviors among homeless youth, outperforming traditional peer leader selection methods.

Contribution

The paper introduces an AI system for selecting peer leaders in HIV prevention efforts and empirically validates its effectiveness through a large-scale clinical trial.

Findings

AI-optimized intervention led to significant risk behavior reduction

Compared to standard methods, AI approach showed superior impact

First empirical validation of AI in community health interventions

Abstract

Youth experiencing homelessness (YEH) are subject to substantially greater risk of HIV infection, compounded both by their lack of access to stable housing and the disproportionate representation of youth of marginalized racial, ethnic, and gender identity groups among YEH. A key goal for health equity is to improve adoption of protective behaviors in this population. One promising strategy for intervention is to recruit peer leaders from the population of YEH to promote behaviors such as condom usage and regular HIV testing to their social contacts. This raises a computational question: which youth should be selected as peer leaders to maximize the overall impact of the intervention? We developed an artificial intelligence system to optimize such social network interventions in a community health setting. We conducted a clinical trial enrolling 713 YEH at drop-in centers in a large US city. The clinical trial compared interventions planned with the algorithm to those where the highest-degree nodes in the youths' social network were recruited as peer leaders (the standard method in public health) and to an observation-only control group. Results from the clinical trial show that youth in the AI group experience statistically significant reductions in key risk behaviors for HIV transmission, while those in the other groups do not. This provides, to our knowledge, the first empirical validation of the usage of AI methods to optimize social network interventions for health. We conclude by discussing lessons learned over the course of the project which may inform future attempts to use AI in community-level interventions.