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Updated: Sep 18, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

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

James T Anibal^1,2, Hannah B Huth³, Tom Boeken⁴

¹Center for Interventional Oncology, NIH Clinical Center, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (NIH), Bethesda, MD, 20892, USA. anibal.james@nih.gov.

Cardiovascular and Interventional Radiology

|June 25, 2025

Summary

This study introduces the iCARE checklist to ensure robust artificial intelligence (AI) systems in interventional radiology (IR). The checklist guides AI development from code to clinic for safer, generalizable IR technologies.

Keywords:

Artificial intelligence (AI)Evaluation checklist Interventional radiology Standards

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Artificial Intelligence Approaches to Assessing Primary Cilia

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

Area of Science:

Medical Imaging and Artificial Intelligence
Interventional Radiology Research

Background:

Artificial intelligence (AI) is increasingly used in interventional radiology (IR).
Ensuring the robustness of AI systems in IR research and practice is crucial.
Novel AI technologies require standardized evaluation before clinical integration.

Purpose of the Study:

To introduce comprehensive standards and an evaluation checklist (iCARE) for AI in IR.
To ensure the robustness and reliability of AI applications in interventional radiology.
To support the development of safe and generalizable AI technologies for IR.

Main Methods:

Development of the iCARE (interventional radiology AI Robustness Evaluation) checklist.
The checklist covers the AI development pipeline from "code-to-clinic".
Key areas include dataset curation, training, explainability, privacy, bias, reproducibility, and deployment.

Main Results:

The iCARE checklist provides a structured framework for evaluating AI in IR.
It addresses critical aspects of AI development and deployment.
Facilitates the assessment of AI systems for clinical use in interventional radiology.

Conclusions:

The iCARE checklist aims to enhance the safety and generalizability of AI in IR.
It supports the integration of AI into IR workflows for improved patient care and outcomes.
Standardized evaluation is essential for trustworthy AI in medical practice.