AI-Generated Mnemonic Images Improve Long-Term Retention of Coronary Artery Occlusions in STEMI: A Comparative Study
Year: 2025
Type: article
Study design: Clinical Trial
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 (ECG). 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 was 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 medical education of image analysis.
Source: Technologies
Authors: Zahraa Alomar, Meize Guo, Tyler Bland
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4410783497