Systematic review of cost effectiveness and budget impact of artificial intelligence in healthcare.

Abstract

This systematic review examines the cost-effectiveness, utility, and budget impact of clinical artificial intelligence (AI) interventions across diverse healthcare settings. Nineteen studies spanning oncology, cardiology, ophthalmology, and infectious diseases demonstrate that AI improves diagnostic accuracy, enhances quality-adjusted life years, and reduces costs-largely by minimizing unnecessary procedures and optimizing resource use. Several interventions achieved incremental cost-effectiveness ratios well below accepted thresholds. However, many evaluations relied on static models that may overestimate benefits by not capturing the adaptive learning of AI systems over time. Additionally, indirect costs, infrastructure investments, and equity considerations were often underreported, suggesting that reported economic benefits may be overstated. Dynamic modeling indicates sustained long-term value, but further research is needed to incorporate comprehensive cost components and subgroup analyses. These findings underscore the clinical promise and economic complexity of AI in healthcare, emphasizing the need for context-specific, methodologically robust evaluations to guide future policy and practice effectively.