Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology

Abstract Background The potential for artificial intelligence (AI) to transform healthcare cannot be ignored, and the development of AI technologies has increased significantly over the past decade. Furthermore, healthcare systems are under tremendous pressure, and efficient allocation of scarce healthcare resources is vital to ensure value for money. Health economic evaluations (HEEs) can be used to obtain information about cost-effectiveness. The literature acknowledges that the conduct of such evaluations differs between medical technologies (MedTechs) and pharmaceuticals, and poor quality evaluations can provide misleading results. This systematic review seeks to map the evidence on the general methodological quality of HEEs for AI technologies to identify potential areas which can be subject to quality improvements. We used the 35-item checklist by Drummond and Jefferson and four additional checklist domains proposed by Terricone et al. to assess the methodological quality of full HEEs of interventions that include AI. Results We identified 29 studies for analysis. The included studies had higher completion scores for items related to study design than for items related to data collection and analysis and interpretation of results. However, none of the studies addressed MedTech-specific items. Conclusions There was a concerningly low number of full HEEs relative to the number of AI publications, however the trend is that the number of studies per year is increasing. Mapping the evidence of the methodological quality of HEEs of AI shows a need to improve the quality in particular the use of proxy measures as outcome, reporting, and interpretation of the ICER.