Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice
Abstract 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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-02-01
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| Series: | Insights into Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13244-023-01599-z |
| _version_ | 1797274382404419584 |
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| author | Bart-Jan Boverhof W. Ken Redekop Daniel Bos Martijn P. A. Starmans Judy Birch Andrea Rockall Jacob J. Visser |
| author_facet | Bart-Jan Boverhof W. Ken Redekop Daniel Bos Martijn P. A. Starmans Judy Birch Andrea Rockall Jacob J. Visser |
| author_sort | Bart-Jan Boverhof |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-07T14:57:35Z |
| format | Article |
| id | doaj.art-87fe5d38172344deabd12d5281bb6095 |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-03-07T14:57:35Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-87fe5d38172344deabd12d5281bb60952024-03-05T19:20:09ZengSpringerOpenInsights into Imaging1869-41012024-02-0115111010.1186/s13244-023-01599-zRadiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practiceBart-Jan Boverhof0W. Ken Redekop1Daniel Bos2Martijn P. A. Starmans3Judy Birch4Andrea Rockall5Jacob J. Visser6Erasmus School of Health Policy and Management, Erasmus University RotterdamErasmus School of Health Policy and Management, Erasmus University RotterdamDepartment of Epidemiology, Erasmus University Medical CentreDepartment of Radiology & Nuclear Medicine, Erasmus University Medical CentrePelvic Pain Support NetworkDepartment of Surgery & Cancer, Imperial College LondonDepartment of Radiology & Nuclear Medicine, Erasmus University Medical CentreAbstract 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.https://doi.org/10.1186/s13244-023-01599-zValue frameworkArtificial intelligenceEvidence-based medicineLocal assessmentValue-based radiology |
| spellingShingle | Bart-Jan Boverhof W. Ken Redekop Daniel Bos Martijn P. A. Starmans Judy Birch Andrea Rockall Jacob J. Visser Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice Insights into Imaging Value framework Artificial intelligence Evidence-based medicine Local assessment Value-based radiology |
| title | Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice |
| title_full | Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice |
| title_fullStr | Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice |
| title_full_unstemmed | Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice |
| title_short | Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice |
| title_sort | radiology ai deployment and assessment rubric radar to bring value based ai into radiological practice |
| topic | Value framework Artificial intelligence Evidence-based medicine Local assessment Value-based radiology |
| url | https://doi.org/10.1186/s13244-023-01599-z |
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