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

Autores de IIS La Fe

• Fernando Gómez Muñoz

  Autor

Participantes ajenos a IIS La Fe

• Anibal, James T.
• Huth, Hannah B.
• Boeken, Tom
• Daye, Dania
• Gichoya, Judy
• Chapiro, Julius
• Wood, Bradford J.
• Sze, Daniel Y.
• Hausegger, Klaus

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.

Keywords

• Interventional radiology; Artificial intelligence (AI); Evaluation checklist; Standards