Interventional Radiology Reporting Standards and Checklist for Artificial Intelligence Research Evaluation (iCARE)

Fecha de publicación: 01/01/2025

Autores de IIS La Fe

Fernando Gómez Muñoz

Autor

Participantes ajenos a IIS La Fe

Anibal J.T.
Huth H.B.
Boeken T.
Daye D.
Gichoya J.
Chapiro J.
Wood B.J.
Sze D.Y.
Hausegger K.

Grupos

Investigacion Biomédica en Imagen (GIBI 2-30)

Abstract

As artificial intelligence (AI) becomes increasingly prevalent within interventional radiology (IR) research and clinical practice, steps must be taken to ensure the robustness of novel technological systems presented in peer-reviewed journals. This report introduces comprehensive standards and an evaluation checklist (iCARE) that covers the application of modern AI methods in IR-specific contexts. The iCARE checklist encompasses the full "code-to-clinic" pipeline of AI development, including dataset curation, pre-training, task-specific training, explainability, privacy protection, bias mitigation, reproducibility, and model deployment. The iCARE checklist aims to support the development of safe, generalizable technologies for enhancing IR workflows, the delivery of care, and patient outcomes. © Cardiovascular and Interventional Radiological Society of Europe (CIRSE) and the Society of Interventional Radiology (SIR) 2025.

Datos de la publicación

ISSN/ISSNe:: 0174-1551, 1432-086X
Tipo:: Article
Páginas:: 1075-1087
DOI:: 10.1007/s00270-024-03956-x
PubMed:: 40560391
Enlace a otro recurso:: www.scopus.com
Factor de Impacto:: 0,707 SCImago ℠
Cuartil:: Q2 SCImago ℠

Documentos

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Métricas

Filiaciones

Anibal J.T.:: Center for Interventional Oncology, NIH Clinical Center, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (NIH), Bethesda, 20892, MD, United States

Computational Health Informatics Lab, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, United Kingdom
Huth H.B.:: Center for Interventional Oncology, NIH Clinical Center, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (NIH), Bethesda, 20892, MD, United States
Boeken T.:: Université Paris Cité, Faculté de Médecine, Paris, 75006, France
Daye D.:: Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
Gichoya J.:: Department of Radiology, Emory University School of Medicine, Atlanta, GA, United States
Muñoz F.G.:: Imagen Médica, Hospital Universitario y Politécnico La Fe/Antoni Van Leeuwenhoek-NKI, Amsterdam, Netherlands
Chapiro J.:: Yale School of Medicine, New Haven, CT, United States
Wood B.J.:: Center for Interventional Oncology, NIH Clinical Center, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (NIH), Bethesda, 20892, MD, United States
Sze D.Y.:: Journal for Vascular and Interventional Radiology, Philadelphia, United States
Hausegger K.:: CardioVascular and Interventional Radiology, Vienna, Austria

Keywords

Artificial Intelligence; Checklist; Humans; Radiology, Interventional; article; artificial intelligence; bias mitigation; checklist; clinical practice; diagnosis; human; interventional radiology; privacy; radiology; reproducibility; treatment outcome; workflow