Conditional Optimism and Systemic Constraints: Healthcare Provider Perceptions of AI in Safety-Net Settings Using a Mixed-Methods Vignette Approach

As health AI tools enter clinical spaces, how they are perceived by providers in safety-net environments shapes whether their benefits reach underserved populations. We examined the degree and drivers of healthcare providers' trust in health AI, whether that trust moderates perceived benefits for safety-net populations, and how providers balance benefit against concern across four specific hypothetical AI tools. We used a two-study mixed-methods design: 18 semi-structured interviews with healthcare practitioners and research leaders, followed by a survey of 229 Texas healthcare providers incorporating four vignettes describing hypothetical AI tools, each rated for appeal and concern and followed by open-ended explanation. Responses were thematically coded and stratified by self-reported trust in health AI (47.6% high trust, 52.4% low trust). Low-trust respondents most often cited building trust (50.5%) and data bias and accuracy (33.0%) as drivers of their current trust level, while high-trust respondents more often cited efficient and effective care (23.5% vs. 3.7% low-trust, p<0.001) and accuracy of data (11.8% vs. 1.8% low-trust, p=0.004); concerns about data security, governance, and the need for human oversight were raised at similar rates by both groups. When asked how AI could specifically benefit safety-net populations, both groups converged on expanding access to care (45.9% each), and none of 25 benefit-related codes differed significantly by trust group. Across all four vignettes, mean appeal ratings exceeded mean concern ratings, and high-trust providers rated every tool as more appealing and, for three of four tools, less concerning, without changing which tool ranked where relative to others. Providers exhibit conditional optimism: support is contingent on governance, human oversight, and context-sensitive implementation rather than on the technology itself. Realizing AI's promise for safety-net populations will require human-in-the-loop models, localized implementation strategies, community-informed design, and engaging providers directly in policy design and tool development.