AI Adoption in Healthcare and Its Broader Societal Impact: National Cost Savings, Productivity Gains, Public Health Improvement, and Environmental Sustainability

Abstract

Artificial intelligence adoption in healthcare is increasingly evaluated not merely in terms of clinical accuracy or narrow return on investment, but in terms of its broader societal impact, spanning national health system cost savings, clinician and administrative productivity gains, population-level public health improvement, and, increasingly, the environmental sustainability of the computing infrastructure that underlies large-scale AI deployment. This article examines AI adoption in healthcare through this broader societal lens, synthesizing a systematic macro-level review of AI's impact on the health economy, workforce productivity, and administrative efficiency, national frameworks for AI-powered precision public health and AI-driven prior authorization transformation, and public health perspectives on health equity and ethical considerations in AI deployment, alongside recent evidence quantifying the environmental and public health costs of the computing infrastructure underlying national-scale AI systems. It further draws on high-resolution petrophysical characterization of geothermal reservoir quality and machine learning-enhanced geomechanical characterization of wellbore stability to examine geothermal energy's role in supporting environmentally sustainable computing infrastructure for AI-driven healthcare. The article concludes by identifying persistent gaps in health equity monitoring, macro-level economic evidence synthesis, and environmental accountability, and proposes a research agenda for advancing a genuinely comprehensive assessment of AI's broader societal impact in healthcare.