Abstract
Abstract
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
Author Summary
Artificial intelligence is increasingly built into the everyday tools that healthcare workers use, but whether it actually helps the patients who need the most support depends on what those workers think of it and how much they trust it. We talked with 18 healthcare workers and researchers, then surveyed 229 healthcare providers across Texas, many of whom work in clinics and hospitals that serve low-income and rural communities. About half of the providers we surveyed trusted health AI and half did not. Providers who trusted AI less pointed to a need to build trust and worries about biased or inaccurate data; providers who trusted it more pointed to gains in efficient, accurate care. Both groups agreed, in similar numbers, that AI’s biggest promise for underserved patients is expanding access to care, and neither group described that promise differently from the other. When we showed providers four realistic AI tools, all of them found more to like than to worry about. Our findings suggest that providers are not simply for or against health AI. Their support depends on whether AI is deployed with real human oversight, sound governance, and attention to the specific setting where it is used.