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

arXiv: https://arxiv.org/abs/2009.09559 · HTML: https://mlsanity.com/paper/2009.09559/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 10 of 10)

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- [Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation](https://mlsanity.com/paper/2302.02570/) (2023)
- [Policy-Embedded Graph Expansion: Networked HIV Testing with Diffusion-Driven Network Samples](https://mlsanity.com/paper/2601.16233/) (2026)
- [Designing Equitable Algorithms](https://mlsanity.com/paper/2302.09157/) (2023)
- [Beyond Listenership: AI-Predicted Interventions Drive Improvements in Maternal Health Behaviours](https://mlsanity.com/paper/2507.20755/) (2025)
- [The Hardness of Achieving Impact in AI for Social Impact Research: A Ground-Level View of Challenges & Opportunities](https://mlsanity.com/paper/2506.14829/) (2025)
- [RelAItionship Building: Analyzing Recruitment Strategies for Participatory AI](https://mlsanity.com/paper/2508.20176/) (2025)

## Abstract (arXiv)

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.