Comfort with AI for HIV Prevention Among Cisgender Women in New York City

Background: Long-acting pre-exposure prophylaxis (PrEP) expands HIV prevention options for women. However, PrEP impact depends on addressing persistent gaps in awareness, access, and use. Artificial intelligence (AI) tools, including conversational agents, are being explored to advance PrEP uptake, but comfort with AI may influence their impact. Thus, we examined women's comfort with AI and its association with PrEP awareness. Methods: We analyzed self-reported data from women aged [≥]18 years in a cross-sectional survey conducted in New York City from August 2023 to August 2024. We performed descriptive analyses, applied latent class analysis to identify AI knowledge/comfort profiles, and estimated unadjusted and adjusted odds ratios to assess associations between profile membership and PrEP awareness. Results: Among 306 respondents without a diagnosis of HIV who completed AI-related survey items, the median age was 36. Most women identified as Hispanic/Latina (60%) or Non-Hispanic Black (18%), had not completed college (53%), and spoke only English or were bilingual (81%). Latent class analysis identified four AI knowledge/comfort profiles that differed by PrEP awareness, race/ethnicity, borough, prior drug use, and technology utilization. Women with varied AI knowledge, broad AI discomfort, and comfort with clinicians maintaining privacy had lower odds of PrEP awareness (OR: 0.35, 95% CI: 0.16-0.75), but this association did not persist after statistical adjustment. Conclusions: PrEP awareness and AI knowledge were limited, yet many women expressed openness to AI-enabled tools when privacy was assured. AI-enabled HIV prevention tools should prioritize trust, transparency, confidentiality, and the lived contexts of the women they intend to serve.