Economic Evaluation of Artificial Intelligence Integration in Global Healthcare: Balancing Costs, Outcomes, and Investment Value

Abstract

Artificial intelligence (AI) is transforming global healthcare by enhancing diagnostic accuracy, operational efficiency, and personalized treatment, while presenting complex economic, ethical, and regulatory challenges. This comprehensive analysis explores the integration of AI technologies, including machine learning, natural language processing, robotics, and decision support systems, across diverse clinical and administrative applications. It examines key economic concepts such as cost-effectiveness, value-based care, and return on investment, highlighting both direct costs related to implementation, training, and maintenance, and indirect benefits including error reduction, shortened hospital stays, and improved workforce productivity. The discussion addresses disparities in AI adoption between high-income and low- and middle-income countries, emphasizing infrastructural barriers, potential for leapfrogging technologies, and the necessity of international collaboration and standardization. Ethical, legal, and social implications are considered alongside technological limitations, data quality, interoperability, and bias mitigation. Financing models such as public-private partnerships, venture capital, and evolving insurance reimbursement frameworks are evaluated for their roles in supporting sustainable AI deployment. Future directions focus on scaling AI solutions globally, integrating AI with emerging technologies like IoT and blockchain, and transforming healthcare workforce roles. The synthesis underscores that realizing AI’s promise in healthcare economics requires balanced investment, robust governance, interdisciplinary collaboration, and continuous evaluation to ensure equitable, efficient, and high-quality care delivery worldwide.

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