Objectives To systematically review the evidence on the cost-effectiveness of artificial intelligence (AI) interventions for diagnostic imaging in radiology, thereby identifying methodological gaps and priorities for future research. Materials and methods A PRISMA-compliant search of PubMed, Cochrane Library, Scopus, Web of Science, and Embase was conducted for English-language studies published until January 23, 2025. Eligible studies evaluated AI-based radiology interventions for diagnostic imaging with formal economic analysis. Studies were appraised using the Consolidated Health Economic Evaluation Reporting Standards for Artificial Intelligence-based Interventions (CHEERS-AI) checklist. Results Of 360 publications identified, ten met the inclusion criteria. Nine studies reported cost-utility analyses using quality-adjusted life-years (QALYs) and one used disability-adjusted life-years (DALYs). All studies employed theoretical modeling (Markov, decision-tree, or hybrid simulations), with no prospective real-world cost-effectiveness data. Applications included cancer screening, acute stroke detection, infection control, and opportunistic detection of incidental findings. Most included studies used publicly available healthcare data from the United States or the United Kingdom to model cost-effectiveness outcomes, and concluded that AI may be cost-effective under model-specific assumptions and willingness-to-pay thresholds. Methodological heterogeneity precluded meta-analysis. Conclusion Current evidence on the cost-effectiveness of AI in radiology is limited, model-based, and lacks validation with real-world prospective data. Future research should employ standardized evaluation frameworks and incorporate empirical clinical data to better inform implementation decisions. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Economic evaluations are important for sustained implementation of AI in radiology, but current evidence on cost-effectiveness is limited. <Emphasis Type="BoldItalic">Findings</Emphasis> Ten studies, predominantly from the United States and the United Kingdom, used model-based analyses and generally indicated cost-effectiveness under study-specific assumptions, without prospective real-world cost data. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> Despite increasing adoption of AI tools in radiology, current cost-effectiveness evidence is limited, wholly model-based, and methodologically heterogeneous. Standardized economic evaluations using prospective real-world data are needed to support informed adoption decisions.

Cost-effectiveness of artificial intelligence tools in radiology: a systematic review

期刊论文
发表日期：

2025年12月24日

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Dana Brin,
Noam Tau

摘要

Objectives

To systematically review the evidence on the cost-effectiveness of artificial intelligence (AI) interventions for diagnostic imaging in radiology, thereby identifying methodological gaps and priorities for future research.

Materials and methods

A PRISMA-compliant search of PubMed, Cochrane Library, Scopus, Web of Science, and Embase was conducted for English-language studies published until January 23, 2025. Eligible studies evaluated AI-based radiology interventions for diagnostic imaging with formal economic analysis. Studies were appraised using the Consolidated Health Economic Evaluation Reporting Standards for Artificial Intelligence-based Interventions (CHEERS-AI) checklist.

Results

Of 360 publications identified, ten met the inclusion criteria. Nine studies reported cost-utility analyses using quality-adjusted life-years (QALYs) and one used disability-adjusted life-years (DALYs). All studies employed theoretical modeling (Markov, decision-tree, or hybrid simulations), with no prospective real-world cost-effectiveness data. Applications included cancer screening, acute stroke detection, infection control, and opportunistic detection of incidental findings. Most included studies used publicly available healthcare data from the United States or the United Kingdom to model cost-effectiveness outcomes, and concluded that AI may be cost-effective under model-specific assumptions and willingness-to-pay thresholds. Methodological heterogeneity precluded meta-analysis.

Conclusion

Current evidence on the cost-effectiveness of AI in radiology is limited, model-based, and lacks validation with real-world prospective data. Future research should employ standardized evaluation frameworks and incorporate empirical clinical data to better inform implementation decisions.

Key Points

Question Economic evaluations are important for sustained implementation of AI in radiology, but current evidence on cost-effectiveness is limited.

Findings Ten studies, predominantly from the United States and the United Kingdom, used model-based analyses and generally indicated cost-effectiveness under study-specific assumptions, without prospective real-world cost data.

Clinical relevance Despite increasing adoption of AI tools in radiology, current cost-effectiveness evidence is limited, wholly model-based, and methodologically heterogeneous. Standardized economic evaluations using prospective real-world data are needed to support informed adoption decisions.