A systematic review of economic evidence of artificial intelligence in healthcare

Robin Van Kessel

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Background: Ambitious claims suggest AI could save $200-360 billion annually in US healthcare and €212 billion in Europe, though the empirical evidence base supporting these projections remains unclear. Objective: This systematic review seeks to synthesise the available economic evidence of AI technologies in healthcare and contextualise the available economic evidence against the broader policy expectations surrounding the economic impact of AI in healthcare. Methods: We searched MEDLINE, Embase, Global Health, PsycINFO, and Cochrane Central for scientific sources. The JBI dominance ranking matrix was used to compare and interpret the results of the included economic evaluations. Methodological quality was assessed using the JBI Critical Appraisal tool of Economic Evaluations and the CHEERS-AI reporting checklist. Results: We identified 16,430 academic records and 1,593 grey literature records, of which 91 records met the inclusion criteria, representing 98 unique evaluations. Of these, 36% demonstrate a clear health economic preference for the AI technology, which increased to 44% when only considering the 51 high-quality studies. AI interventions were mainly designed for healthcare providers, with ophthalmology and oncology being the most common. Conclusions: The high-quality studies show definite potential for positive cost-effectiveness and economic impact of AI technologies, though we cannot yet support the ambitious claims that AI technologies can translate to hundreds of billions in cost-savings. When combining our findings with those of randomised controlled trials evaluating AI technologies in clinical practice, it becomes evident that AI technologies frequently yield improvements in terms of clinical and economic outcomes or match the current standard of care.

Keywords: artificial intelligence; machine learning; economic evaluation; systematic review; digital health; healthcare; health services (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2026-10-01
References: Add references at CitEc
Citations:

Published in Health Policy, 1, October, 2026, 172. ISSN: 0168-8510

Downloads: (external link)
https://researchonline.lse.ac.uk/id/eprint/140310/ Open access version. (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:140310

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().