Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum...

Area of Science:

• Radiology
• Artificial Intelligence
• Medical Education

Background:

• Existing artificial intelligence (AI) curricula in radiology lack focus on pediatric-specific applications.
• Pediatric radiology practice patterns differ significantly from adult radiology, impacting AI model generalizability.

Purpose of the Study:

• To highlight the necessity for a tailored AI curriculum in pediatric radiology.
• To identify key considerations for developing and implementing AI in pediatric imaging.

Main Methods:

• Review of current AI educational offerings in radiology.
• Analysis of unique aspects of pediatric radiology practice relevant to AI.
• Identification of challenges in pediatric AI research and clinical use.

Main Results:

• Major radiology societies offer general AI training, but pediatric-specific content is absent.
• Pediatric AI education must account for differences in imaging modalities, acquisition parameters, radiation doses, child development, and disease prevalence.
• Challenges in pediatric AI include data governance, ethical considerations, and clinical implementation.

Conclusions:

• A dedicated AI curriculum for pediatric radiology is essential for advancing the field.
• Curricula must integrate pediatric-specific challenges, AI model evaluation, and ethical guidelines.
• Addressing these aspects will enhance radiologists' competence in applying AI to pediatric patients.