Artificial Intelligence in Health Economics: Opportunities and Challenges for the Future: A Targeted Literature Review
Author(s)
George Gourzoulidis, PhD1, Catherine Kastanioti, PhD1, George Mavridoglou, PhD2, Thodoris Kotsilieris, PhD1, Dikaios Voudigaris, MSc3, Charalampos Tzanetakos, MSc4.
1Department of Business and Organizations Administration, University of the Peloponnese, Kalamata, Greece, 2Department of Accounting and Finance, University of the Peloponnese, Kalamata, Greece, 3Health Through Evidence, Athens, Greece, 4Health Through Evidece, Athens, Greece.
1Department of Business and Organizations Administration, University of the Peloponnese, Kalamata, Greece, 2Department of Accounting and Finance, University of the Peloponnese, Kalamata, Greece, 3Health Through Evidence, Athens, Greece, 4Health Through Evidece, Athens, Greece.
OBJECTIVES: To evaluate the economic implications of artificial intelligence (AI) integration in healthcare, with a focus cost-efficiency, resource optimization, healthcare equity, and impact on insurance and reimbursement frameworks. This targeted literature review synthesizes existing evidence to support policy development and promote economic sustainability in AI-enabled healthcare systems.
METHODS: A targeted literature review was conducted using PubMed, Scopus, and Web of Science databases, covering studies published up to May 2025. Articles were selected based on predefined eligibility criteria. Supplementary grey literature sources were also reviewed to capture comprehensive insights. Extracted data were analyzed and synthesized to identify key economic and policy implications of AI deployment in healthcare.
RESULTS: Six studies met inclusion criteria. Findings indicated significant potential for economic efficiency, with AI-driven applications projecting annual cost savings ranging from $200 to $360 billion in the U.S. healthcare system. Cost reductions were primarily attributed to enhanced diagnostic accuracy, early intervention capabilities, and administrative task automation. Predictive algorithms notably improved patient stratification and resource allocation, achieving reductions in avoidable hospitalizations by up to 12%. Additionally, AI-enhanced actuarial modeling improved precision in risk adjustment within value-based insurance schemes, although concerns regarding algorithmic bias and equitable data usage were identified. Adoption and regulatory preparedness varied significantly, with public healthcare systems emphasizing equitable access, whereas private systems prioritized operational efficiency.
CONCLUSIONS: AI has a substantial potential to reshape health economics by enhancing efficiency, reducing resource waste, and enabling personalized care delivery. However, unlocking these benefits depends on robust egulatory oversight, comprehensive data management strategies, and the development of equitable policy frameworks. To fully realize AI’s economic value in healthcare, policymakers must address critical challenges such as algorithmic transparency, ethical accountability, and health equity. Strategic investments in digital infrastructure, regulatory capacity, and interdisciplinary workforce training are essential prerequisites for building sustainable, value-driven, AI-enabled healthcare systems.
METHODS: A targeted literature review was conducted using PubMed, Scopus, and Web of Science databases, covering studies published up to May 2025. Articles were selected based on predefined eligibility criteria. Supplementary grey literature sources were also reviewed to capture comprehensive insights. Extracted data were analyzed and synthesized to identify key economic and policy implications of AI deployment in healthcare.
RESULTS: Six studies met inclusion criteria. Findings indicated significant potential for economic efficiency, with AI-driven applications projecting annual cost savings ranging from $200 to $360 billion in the U.S. healthcare system. Cost reductions were primarily attributed to enhanced diagnostic accuracy, early intervention capabilities, and administrative task automation. Predictive algorithms notably improved patient stratification and resource allocation, achieving reductions in avoidable hospitalizations by up to 12%. Additionally, AI-enhanced actuarial modeling improved precision in risk adjustment within value-based insurance schemes, although concerns regarding algorithmic bias and equitable data usage were identified. Adoption and regulatory preparedness varied significantly, with public healthcare systems emphasizing equitable access, whereas private systems prioritized operational efficiency.
CONCLUSIONS: AI has a substantial potential to reshape health economics by enhancing efficiency, reducing resource waste, and enabling personalized care delivery. However, unlocking these benefits depends on robust egulatory oversight, comprehensive data management strategies, and the development of equitable policy frameworks. To fully realize AI’s economic value in healthcare, policymakers must address critical challenges such as algorithmic transparency, ethical accountability, and health equity. Strategic investments in digital infrastructure, regulatory capacity, and interdisciplinary workforce training are essential prerequisites for building sustainable, value-driven, AI-enabled healthcare systems.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MT5
Topic
Medical Technologies
Disease
No Additional Disease & Conditions/Specialized Treatment Areas