An Exploration of the Promises and Perils of Responsible Deployment of Health AI for Safety Net Populations as Perceived by Healthcare Providers

Abstract The current study examines the responsible deployment of AI in healthcare settings with a particular focus on underserved, safety net populations. We employ a mixed methods approach to study the perceptions of health care providers relative to the promises of responsible deployment of AI and the potential perils that need to be navigated to achieve that goal. As health AI tools continue to enter clinical spaces, understanding how they are perceived by providers in safety-net environments is essential for equitable implementation. This study revealed that while there is cautious optimism among healthcare professionals—particularly regarding improvements in workflow, personalization, and efficiency—significant concerns remain around data integrity, trust, and infrastructural readiness. High-trust providers viewed AI as a valuable support system, whereas low-trust providers raised critical questions about governance, privacy, and the risk of exacerbating existing inequities. The findings emphasize the importance of human-in-the-loop models, localized implementation strategies, and community-informed design to ensure that the promise of AI does not bypass the populations it seeks to serve. Moving forward, engaging providers in policy design, tool development, and implementation processes will be crucial for realizing the equitable integration of AI in healthcare.