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Revolutionizing radiology education: exploring and implementing spaced repetition in radiology teaching and curricula
Kaelan Yao1, Jeffers Nguyen2, Mahan Mathur2
1Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
Objective:
This manuscript explores spaced repetition (SR), a revolutionary learning technique, within radiology education to address the challenges of rapidly expanding medical knowledge and condensed training schedules. It also offers a practical guide on implementing SR within radiology curricula.
Summary:
SR is an evidence-based learning strategy that optimizes long-term retention by scheduling information reviews at scheduled intervals. While widely used in other medical domains, its adoption in radiology has been slowed by the field's inherently image-based nature. At each level of expertise, SR can serve as a powerful study tool. Furthermore, artificial intelligence (AI) serves as a powerful adjunct to SR. AI can help develop flashcards based on both curricula and current literature, allowing rapid scalability of existing material to the SR format.
Conclusion:
SR, primarily through platforms like Anki, offers a scalable and efficient adjunct to traditional radiology curricula. When integrated thoughtfully, it fosters sustained competence and mastery across all levels of radiological practice.
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