Artificial intelligence reporting guidelines: what the pediatric radiologist needs to know.
Artificial intelligence
Children
Diagnostic accuracy
Machine learning
Pediatric radiology
Reporting guidelines
Journal
Pediatric radiology
ISSN: 1432-1998
Titre abrégé: Pediatr Radiol
Pays: Germany
ID NLM: 0365332
Informations de publication
Date de publication:
10 2022
10 2022
Historique:
received:
03
03
2021
accepted:
10
06
2021
revised:
06
05
2021
pubmed:
2
7
2021
medline:
12
10
2022
entrez:
1
7
2021
Statut:
ppublish
Résumé
There has been an exponential rise in artificial intelligence (AI) research in imaging in recent years. While the dissemination of study data that has the potential to improve clinical practice is welcomed, the level of detail included in early AI research reporting has been highly variable and inconsistent, particularly when compared to more traditional clinical research. However, inclusion checklists are now commonly available and accessible to those writing or reviewing clinical research papers. AI-specific reporting guidelines also exist and include distinct requirements, but these can be daunting for radiologists new to the field. Given that pediatric radiology is a specialty faced with workforce shortages and an ever-increasing workload, AI could help by offering solutions to time-consuming tasks, thereby improving workflow efficiency and democratizing access to specialist opinion. As a result, pediatric radiologists are expected to be increasingly leading and contributing to AI imaging research, and researchers and clinicians alike should feel confident that the findings reported are presented in a transparent way, with sufficient detail to understand how they apply to wider clinical practice. In this review, we describe two of the most clinically relevant and available reporting guidelines to help increase awareness and engage the pediatric radiologist in conducting AI imaging research. This guide should also be useful for those reading and reviewing AI imaging research and as a checklist with examples of what to expect.
Identifiants
pubmed: 34196729
doi: 10.1007/s00247-021-05129-1
pii: 10.1007/s00247-021-05129-1
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
2101-2110Subventions
Organisme : Department of Health
ID : NIHR-CDF-2017-10-037
Pays : United Kingdom
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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