Educational Competencies for Artificial Intelligence in Radiology: A Scoping Review.

Area of Science:

• Medical Imaging
• Artificial Intelligence in Healthcare
• Radiology Education

Background:

• Artificial intelligence (AI) is increasingly integrated into radiology practice.
• There's a lack of clarity on essential competencies for radiologists regarding AI.
• Radiology residency programs need guidance on AI-related skill development.

Purpose of the Study:

• To identify crucial knowledge, skills, and attitudes for radiologists using AI.
• To inform the development of AI competencies in radiology education.

Main Methods:

• A scoping review was conducted using Arksey and O'Malley's methodology.
• Searched major electronic databases (PubMed, Embase, Scopus, ERIC) for articles from 2010-2024.
• Two independent reviewers screened 5920 articles, with 49 meeting inclusion criteria for data extraction.

Main Results:

• Identified core competencies in AI model development, evaluation, clinical implementation, bias, ethics, and regulation.
• Found diverse perspectives on competencies, focusing either on technical AI development or clinical application.
• Highlighted heterogeneity in current AI educational programs for radiologists.

Conclusions:

• A consensus on essential AI knowledge, skills, and attitudes for radiologists is currently lacking.
• Substantial heterogeneity exists in AI educational content within radiology residency programs.
• Further research is needed to establish core competencies and integrate AI training into radiology residency programs.