Artificial Intelligence and Its Effect on Radiology Residency Education: Current Challenges, Opportunities, and...

Area of Science:

• Radiology
• Medical Education
• Artificial Intelligence

Background:

• Artificial intelligence (AI) is increasingly integrated into radiology, impacting workflows and clinical decisions.
• Current radiology residency curricula face challenges in accommodating comprehensive AI education due to existing demands.

Purpose of the Study:

• To discuss the essential components for effective resident training in AI: education, AI education tools, and clinical implementation.
• To explore strategies for overcoming curriculum limitations and preparing residents for AI in clinical practice.

Main Methods:

• Review of current challenges in AI education for radiology residents.
• Discussion of potential solutions including distinct educational tracks and external courses.
• Analysis of the role of AI-powered educational tools and their impact on learning.
• Consideration of ethical implications and the need for structured mentorship.

Main Results:

• Overcrowded curricula present a significant barrier to thorough AI education.
• Longitudinal educational tracks and external courses can enhance AI knowledge acquisition.
• AI-driven educational tools offer novel methods for active learning and improved comprehension.
• The proliferation of FDA-approved AI tools necessitates resident preparedness for clinical integration.

Conclusions:

• Residency programs must adapt curricula to include evidence-based AI education.
• Structured instruction on AI fundamentals, tools, and ethics is vital for future radiologists.
• Training should empower residents to critically evaluate and utilize AI, ensuring it enhances, not replaces, clinical expertise.