systematic review

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

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Latest posts

Self-reporting with checklists in AI research on medical imaging

This study aimed to evaluate the usage of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM), a well-known and widely adopted checklist in the radiological community, for self-reporting through a systematic analysis of its citations. The authors used three databases (Google Scholar, Web of Science, and Scopus) and identified nearly 400 unique citations across 118 papers, of which only

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Utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics

The aim of this study was to provide an updated systematic review of radiomics in osteosarcoma, utilizing various databases such as PubMed, Embase, China National Knowledge Infrastructure, and more. Articles found in these databases were assessed by Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Checklist for Artificial Intelligence in

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EuSoMII Radiomics Auditing Group Initiative: Review of RQS applications

When the Radiomics Quality Score (RQS) was presented to the scientific community back in 2017, its authors aimed to introduce a tool for a rapid and effective evaluation of radiomics studies’ scientific/clinical merit. Conceived as a quality seal to be published alongside presented results and newly proposed radiomics models, the RQS has instead mostly been adopted by researchers as a

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Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol

The review was prospectively registered on PROSPERO and adheres to the PRISMA protocols. We decided to undertake the systematic review because while there is huge hype around potential clinical applications of artificial intelligence (AI) in radiology, there is a chasm between the promise and the implementation. We also felt that many papers in the field deal with a narrow range

Read More →

Self-reporting with checklists in AI research on medical imaging

This study aimed to evaluate the usage of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM), a well-known and widely adopted checklist in the radiological community, for self-reporting through a systematic analysis of its citations. The authors used three databases (Google Scholar, Web of Science, and Scopus) and identified nearly 400 unique citations across 118 papers, of which only

Read More →

Utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics

The aim of this study was to provide an updated systematic review of radiomics in osteosarcoma, utilizing various databases such as PubMed, Embase, China National Knowledge Infrastructure, and more. Articles found in these databases were assessed by Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Checklist for Artificial Intelligence in

Read More →

EuSoMII Radiomics Auditing Group Initiative: Review of RQS applications

When the Radiomics Quality Score (RQS) was presented to the scientific community back in 2017, its authors aimed to introduce a tool for a rapid and effective evaluation of radiomics studies’ scientific/clinical merit. Conceived as a quality seal to be published alongside presented results and newly proposed radiomics models, the RQS has instead mostly been adopted by researchers as a

Read More →

Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol

The review was prospectively registered on PROSPERO and adheres to the PRISMA protocols. We decided to undertake the systematic review because while there is huge hype around potential clinical applications of artificial intelligence (AI) in radiology, there is a chasm between the promise and the implementation. We also felt that many papers in the field deal with a narrow range

Read More →

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Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
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03
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04
European Radiology, Insights into Imaging, European Radiology Experimental.