Background: Healthcare systems globally are under increasing financial and operational strain due to aging populations, rising expenditures, and workforce shortages. Amid these challenges, artificial intelligence (AI) has emerged as a promising tool to enhance system-level performance, particularly in cost reduction, productivity gains, and administrative efficiency. Objective: This review aims to synthesize existing evidence on the macro and system-level impact of AI implementation across three key domains: the health economy, workforce productivity, and administrative efficiency. Methods: A systematic review methodology was employed, allowing for the integration of diverse data sources and study types. Literature was systematically identified through PubMed and Google, covering publications from 2020 to July 2025. Studies were included if they evaluated AI's impact on national or regional health expenditure, labor restructuring, or process efficiency. Thematic synthesis was guided by a conceptual framework modeling AI as a system-wide catalyst. Results: Twenty-four studies were included. AI implementation demonstrated potential cost savings of 5-10% in national health expenditures, driven by automation in hospital operations and administrative processes. AI-supported interventions reduced diagnostic time by up to 90% and treatment costs by over 30% in specific applications, such as cancer diagnosis and radiotherapy. Administrative tools, including AI-assisted documentation and claims processing, achieved efficiency gains of up to 40%. However, reliance on simulated models, short-term studies, and single-center data limits generalizability. Conclusions: AI presents significant potential to enhance health system efficiency and reduce costs. Real-world implementation studies, standardized outcome metrics, and robust governance frameworks are essential to validate these gains and ensure equitable, sustainable adoption.
Lee et al. (Tue,) studied this question.