New Research Finds Artificial Intelligence Could Save Billions in Global Healthcare

New research has uncovered the enormous financial potential of artificial intelligence (AI) to transform global healthcare systems-delivering major cost savings, reducing waste, and improving economic efficiency for more than 2 billion people worldwide. If implemented at scale, the findings suggest that AI could help healthcare systems save over $300 billion annually by streamlining operations and eliminating low-value care. Published in Cureus, the study titled "The Impact of Artificial Intelligence on Financial Systems in Healthcare: A Systematic Review of Economic Evaluation Studies" was authored by Shyamsunder Kakatum Rao, Prashant Gupta, Asadullah Mohammed, Kanwarjit Zakhmi, Manas Ranjan Mohanty, and Priji Prasad Jalaja. The authors evaluated the financial outcomes of AI across multiple real-world healthcare deployments, making it one of the most rigorous economic analyses in the field to date.

"This research makes one thing clear-AI is not only a medical breakthrough but also a financial one," said Lukas Meier, Senior Journalist at Alpine Vision Media. "As health systems around the world battle rising costs, workforce shortages, and outdated infrastructure, AI is proving to be one of the few tools capable of delivering both clinical and economic impact." The study examined 341 records sourced from PubMed, Scopus, Web of Science, Embase, and EconLit. Of these, six met the strict inclusion criteria for economic evaluation in healthcare settings. The results were striking: in one case, AI tools helped eliminate over 45,000 unnecessary diagnostic tests in just 45 days; another program saved $12.9 million annually in U.S. Medicaid costs by optimizing care pathways and reducing inefficiencies.

If these AI-driven interventions were replicated across larger populations and healthcare systems, the resulting cost savings would be enormous-particularly for publicly funded systems struggling to balance quality with access. Yet, the authors also found that methodological gaps in current economic evaluation studies-such as unclear assumptions, missing long-term data, and limited ethical transparency-are hindering further adoption and scaling. Only half of the evaluated studies were judged to have a low risk of bias, underscoring the urgent need for standardized frameworks that can support reliable, repeatable cost analyses. These frameworks would guide decision-makers in hospitals, insurance networks, and government agencies on how best to invest in AI and track its real-world value.

"This study is both a wake-up call and a roadmap," Meier noted. "Without a clear way to measure the financial performance of AI tools, we risk letting some of the most transformative technologies in medicine sit on the shelf." Experts including Dr. Tendai Mupunga of Zimbabwe's Health Economics Task Force and Prof. Abigail Newton of Australia's AI in Public Health Initiative have echoed the call for transparent economic modeling, especially in regions where every dollar must deliver maximum patient impact.

"As this research shows, AI has already demonstrated powerful results in isolated settings," Meier concluded. "With the right economic models in place, we can scale these tools globally-and build smarter, stronger, and more financially sustainable healthcare systems for everyone."

Citation: Kakatum Rao S, Gupta P, Mohammed A, et al. (June 18, 2025) The Impact of Artificial Intelligence on Financial Systems in Healthcare: A Systematic Review of Economic Evaluation Studies. Cureus 17(6): e86279. DOI: 10.7759/cureus.86279

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