Evidence map›Paper›PMID 38315288›Full record

ArticleInsights into imaging2024

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

Bart-Jan Boverhof, W Ken Redekop, Daniel Bos, Martijn P A Starmans, Judy Birch, Andrea Rockall, Jacob J Visser

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Bart-Jan BoverhofErasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands.
W Ken RedekopErasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands.
Daniel BosDepartment of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands.
Martijn P A StarmansDepartment of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands.
Judy BirchPelvic Pain Support Network, Dorset, UK.
Andrea RockallDepartment of Surgery & Cancer, Imperial College London, London, UK.
Jacob J VisserDepartment of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands. j.j.visser@erasmusmc.nl.ORCID http://orcid.org/0000-0001-9935-2097

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology.

methodsThis 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.

resultsRADAR 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.

conclusionThe 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.

Indexed as

Artificial intelligenceEvidence-based medicineLocal assessmentValue-based radiologyValue framework

Identifiers

PMID38315288
PMCPMC10844175

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.