<p>The U.S. healthcare system is plagued by systemic challenges, including corporate dominance and a profound erosion of public trust in institutions such as the CDC and FDA. This crisis, rooted in inefficiencies, inequities, and a profit-centric model, has left millions without adequate care, exacerbating health disparities and fueling a public health emergency. This paper presents an argument that while artificial intelligence (AI) applications in healthcare governance could potentially remedy this problematic situation, its implementation is fraught with challenges. The article offers an on-point discussion of various governance weaknesses that exist within the U.S. health system, but it also critically examines whether AI technology genuinely has the potential to tackle deep-rooted systematic and institutional failures. AI might mitigate these significant institutional issues in some contexts but could also inadvertently exacerbate them in others if not implemented with rigorous oversight. A particularly unsatisfactory aspect of current proposals is the inadequate and superficial address of data quality and accessibility concerns—which are absolutely essential for the successful implementation of AI. Given that both the data infrastructure and legal frameworks currently in place are insufficient, this paper argues that a more critical and nuanced analysis is required to navigate the practical and ethical challenges facing healthcare transformation. This vision requires dismantling the problematic aspects of a profit-driven model and addressing the moral and structural failures that have left the U.S. lagging behind its peers, with a clear understanding that AI is a tool that requires careful, ethical, and equitable implementation.</p>