The Impact of Artificial Intelligence on Financial Systems in Healthcare: A Systematic Review of Economic Evaluation...

The integration of artificial intelligence (AI) into healthcare systems has emerged as a transformative approach to addressing rising costs and inefficiencies. While AI applications show promise in improving financial outcomes, the evidence remains fragmented due to methodological heterogeneity and inconsistent reporting. This systematic review aims to synthesize economic evaluations of AI in healthcare, assessing its impact on cost savings, efficiency gains, and cost-effectiveness while identifying gaps in the current literature. Following PRISMA 2020 guidelines, we conducted a systematic search across five databases (PubMed/MEDLINE, Embase, Scopus, Web of Science, and EconLit), identifying 341 records. After removing duplicates and screening for eligibility, six studies met the inclusion criteria, which focused on AI-driven economic evaluations in healthcare settings. Data were extracted using a standardized form, and methodological quality was assessed using the Quality of Health Economic Studies (QHES) tool. A narrative synthesis was performed due to the heterogeneity of study designs and outcomes. The included studies demonstrated significant cost savings, such as reducing unnecessary diagnostic tests by 45,247 in 45 days and lowering Medicaid expenditures by up to United States Dollar (USD) 12.9 million annually. AI also improved cost-effectiveness, though some trade-offs in clinical outcomes were noted. However, methodological limitations were prevalent, including unclear perspectives, a lack of sensitivity analyses, and insufficient discussion of ethical implications. Risk of bias assessment revealed that only three of the six studies had low bias, while others exhibited moderate bias due to these limitations. AI holds substantial potential to enhance financial sustainability in healthcare, but the evidence base is limited by methodological inconsistencies and a lack of long-term evaluations. Standardized frameworks for economic assessments of AI are urgently needed to ensure reliable, equitable, and scalable implementations. Future research should prioritize longitudinal studies, stakeholder engagement, and transparent reporting to bridge the gap between AI innovation and healthcare system priorities.