AI-Generated Mnemonic Images Improve Long-Term Retention of Coronary Artery Occlusions in STEMI: A Comparative Study

Abstract

Medical students face significant challenges retaining complex information, such as interpreting ECGs for coronary artery occlusions, amidst demanding curricula. While artificial intelligence (AI) is increasingly used for medical image analysis, this study explored using generative AI (DALLE-3) to create mnemonic-based images to enhance human learning and retention of medical images, in particular, electrocardiograms (ECGs). This study is among the first to investigate generative AI as a tool not for automated diagnosis but as a human-centered educational aid designed to enhance long-term retention in complex visual tasks like ECG interpretation. We conducted a comparative study with 275 first-year medical students across six campuses; an experimental group (n = 40) received a lecture supplemented with AI-generated mnemonic ECG images, while control groups (n = 235) received standard lectures with traditional ECG diagrams. Student achievement and retention were assessed by course examinations, and student preference and engagement were measured using the Situational Interest Survey for Multimedia (SIS-M). Control groups showed a significant decline in scores on the relevant exam question over time, whereas the experimental group’s scores remained stable, indicating improved long-term retention. Experimental students also reported significantly higher situational interest in the mnemonic-based images over traditional images. AI-generated mnemonic images can effectively improve long-term retention of complex ECG interpretation skills and enhance student engagement and preference, highlighting generative AI’s potential as a valuable cognitive tool in image analysis during medical education.

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