Artificial Intelligence Curriculum Needs Assessment for a Pediatric Radiology Fellowship Program: What, How, and Why?

Area of Science:

• Medical imaging and diagnostics
• Artificial intelligence in healthcare
• Radiology education

Background:

• Artificial intelligence (AI) offers significant potential to advance pediatric radiology practice in patient care, efficiency, and innovation.
• A critical gap exists in formalized AI education and dedicated training curricula for radiologists.
• This study addresses the need for AI training by assessing requirements for a pediatric radiology AI curriculum.

Purpose of the Study:

• To conduct a needs assessment for developing an artificial intelligence (AI) curriculum tailored for pediatric radiology training and continuing education.

Main Methods:

• A focus group study utilized semistructured interviews with radiology trainees and attending radiologists.
• Interviews explored perceptions of AI, competence in interpreting AI literature, and expectations for AI educational programs.
• Data were audio-recorded, transcribed, and subjected to thematic analysis.

Main Results:

• Seven participants engaged in the virtual focus group, revealing themes of AI knowledge, prior training, learning preferences, expectations, and concerns.
• Participants reported no formal AI training, with diverse needs and interests.
• A strong preference for case-based learning emerged, alongside expressed needs for improved understanding of AI's clinical application and literature interpretation.

Conclusions:

• Heterogeneity in AI perspectives necessitates a flexible curriculum addressing varied interests and needs.
• Integrating AI education with research methods, literature critique, and quality control scenarios can build foundational competency.
• A well-designed curriculum is crucial for equipping pediatric radiologists with the skills to effectively utilize AI in practice.