TY - JOUR
T1 - Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice
AU - Boverhof, Bart Jan
AU - Redekop, W. Ken
AU - Bos, Daniel
AU - Starmans, Martijn P.A.
AU - Birch, Judy
AU - Rockall, Andrea
AU - Visser, Jacob J.
N1 - This article belongs to the thematic series entitled “Translating radiological research into practice – from discovery to clinical impact” (Guest Editor: Marion Smits (Rotterdam/NL)).
Publisher Copyright:
© The Author(s) 2024.
PY - 2024/2/5
Y1 - 2024/2/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85184436459&partnerID=8YFLogxK
U2 - 10.1186/s13244-023-01599-z
DO - 10.1186/s13244-023-01599-z
M3 - Article
C2 - 38315288
AN - SCOPUS:85184436459
SN - 1869-4101
VL - 15
JO - Insights into Imaging
JF - Insights into Imaging
IS - 1
ER -