Economic Value of AI in Radiology: A Systematic Review

Abstract

Purpose To summarize the evidence of artificial intelligence's (AI's) economic value across the radiologic workflow. Materials and Methods A comprehensive search of PubMed, Business Source Ultimate, and EconLit was conducted for original research articles published between January 2010 and November 2024. Medical Subject Headings and keywords included "artificial intelligence/machine learning/deep learning/natural language processing," "radiology," and "economic value/cost/budget/revenue/efficiency." Studies were selected based on explicit quantification of economic outcomes, excluding those with only soft outcome criteria like time savings without cost quantification. Study quality was assessed using the Criteria for Health Economic Quality Evaluation. Results From the initial 1879 search results, 21 studies (1%) met the inclusion criteria. The majority evaluated machine learning tools (10 of 21[48%], nine on deep learning), followed by computer-assisted diagnosis (CAD, seven of 21 [33%]), natural language processing (NLP, two of 21 [10%]), and hypothetical AI models (two of 21 [10%]). AI demonstrated economic value through cost savings or incremental cost-effectiveness ratios in resource-intensive tasks, when accuracy matched human performance and costs were fixed. For instance, AI-based lung cancer screening achieved incremental cost savings of up to $242 U.S. dollars (USD) per patient. AI increased costs when specificity was lower than humans' or when using pay-per-use models, as observed with CAD systems raising mammography screening costs by up to $19 USD per patient. In fast tasks such as radiograph evaluations, AI showed value in settings with radiologist shortages. AI reduced costs through protocol optimization and increased revenue via improved follow-up compliance. Conclusion AI's value in radiology is context dependent, varying with task complexity, examination volume, and implementation model. Further high-quality economic evaluations are essential. <b>Keywords:</b> Cost-effectiveness, Efficacy Studies, Artificial Intelligence, Radiology, Healthcare Economics, Systematic Review <i>Supplemental material is available for this article.</i> © The Author(s) 2025. Published by the Radiological Society of North America under a CC BY-NC-ND license. See also the commentary by Amindarolzarbi and Siegel in this issue.

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