Workflow-embedded AI as a cognitive scaffold: A randomized trial on knowledge retention and diagnostic competency in undergraduate radiology education

Abstract

Background: Traditional didactic methods in medical imaging education, predominantly reliant on static images (non-augmented, traditional PACS workflow that requires manual, unguided search and interpretation), consistently fail to bridge the theory-practice divide, contributing to high diagnostic error rates. While the integration of artificial intelligence (AI) with Picture Archiving and Communication Systems (PACS+AI) offers transformative potential, robust evidence quantifying its impact on longitudinal competency development remains scarce. Objective: This study aims to quantitatively evaluate the efficacy of a cognitively optimized PACS+AI framework versus conventional PACS in enhancing radiology education across four critical domains: theoretical knowledge, clinical decision-making competencies, AI acceptance, and knowledge retention. Methods: In a prospective single-blind randomized controlled trial (RCT), 110 medical imaging undergraduates were randomized to PACS+AI (n = 55) or standard PACS (n = 55) groups. Theoretical knowledge was assessed using validated item-bank assessments; clinical decision-making competencies were evaluated through lesion detection, anatomical localization, diagnostic accuracy, and report completeness; AI acceptance was measured using the Technology Acceptance Model (TAM); and knowledge retention was tracked through immediate, 1-month, and 3-month follow-up assessments. The PACS+AI framework provided three core cognitive support functions: automated lesion annotation, structured diagnostic prompting, and workflow-contextualized feedback. Results: The PACS+AI group demonstrated significantly superior outcomes across all domains: theoretical knowledge retention was substantially higher (79.3 % vs. 19.7 % at 3 months, P < 0.001, d=1.95); clinical decision-making competencies showed progressive improvement with large effect sizes (Δ=12.4-18.1, all P < 0.001, d=1.88-2.48); AI acceptance scores were significantly elevated across all TAM constructs (all P < 0.001, d>1.9); and knowledge retention was maintained longitudinally with amplified effects over time. Conclusion: The PACS+AI framework significantly enhances radiology education by optimizing cognitive load distribution, resulting in sustained knowledge retention, superior clinical decision-making competencies, and heightened AI acceptance. This integrated teaching model effectively bridges the gap between theory and practice, cultivates professionals adaptable to the artificial intelligence environment, and aligns with the core needs of the new generation of medical education.

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