Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice.

Autor: Boverhof BJ; Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands., Redekop WK; Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands., Bos D; Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands.; Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands., Starmans MPA; Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands., Birch J; Pelvic Pain Support Network, Dorset, UK., Rockall A; Department of Surgery & Cancer, Imperial College London, London, UK., Visser JJ; Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands. j.j.visser@erasmusmc.nl.
Jazyk: angličtina
Zdroj: Insights into imaging [Insights Imaging] 2024 Feb 05; Vol. 15 (1), pp. 34. Date of Electronic Publication: 2024 Feb 05.
DOI: 10.1186/s13244-023-01599-z
Abstrakt: Objective: To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology.
Methods: This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value.
Results: RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring.
Conclusion: The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology.
Critical Relevance Statement: The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation.
Keypoints: • Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap.
(© 2024. The Author(s).)
Databáze: MEDLINE
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