Advancing Radiology Education with Artificial Intelligence: Curriculum Planning, Implementation, and Evaluation

Area of Science:

• Radiology Education
• Artificial Intelligence in Medicine
• Medical Training

Background:

• Artificial intelligence (AI) integration in clinical radiology necessitates teaching its principles to trainees.
• The transformative potential of AI in radiology education is underexplored.

Purpose of the Study:

• To review how AI can enhance radiology education across curriculum development stages.
• To examine current and future AI applications in radiology training.

Main Methods:

• Systematic review guided by Harden's 10-step curriculum development framework.
• Analysis of AI applications, including generative models, NLP, and simulations.
• Examination of AI's role in needs assessment, personalized learning, content generation, and assessment.

Main Results:

• AI can automate needs assessments, personalize learning pathways, and generate educational content like synthetic cases and board-style questions.
• AI enhances teaching via simulations, objective assessments, and administrative task automation.
• Limitations include high costs, rapid technological change, AI bias, and data privacy concerns.

Conclusions:

• AI offers a paradigm shift in medical education, with immense potential for radiology training.
• Radiology programs should adopt AI strategically, starting with low-risk applications.
• Proactive AI adoption is essential to prepare radiologists for an AI-integrated future.