Current Evidence, Challenges and Future Directions Associated with Teaching Electrocardiogram Interpretation to Medical Students

Abstract

The electrocardiogram (ECG) remains one of the most universally applied diagnostic tools in clinical medicine, yet a substantial body of evidence demonstrates that ECG interpretation competency among medical graduates is persistently and widely inadequate. This critical review examines the current state of ECG education in undergraduate medical curricula, synthesising peer-reviewed evidence published predominantly between 2003 and 2024. The review appraises both traditional and technology-enhanced teaching modalities, explores the cognitive frameworks that underpin ECG literacy, and evaluates the validity and reliability of existing assessment approaches. Evidence indicates that conventional lecture-based instruction, whilst foundational, is insufficient on its own to develop or sustain competent interpretation skills. Technology-enhanced methods—including e-learning platforms, virtual patient simulations, mobile applications, and gamified learning environments—have demonstrated measurable gains in accuracy and confidence, particularly when combined with deliberate practice and structured feedback. Artificial intelligence is emerging not only as a clinical adjunct to ECG interpretation but also as a potential educational instrument capable of personalising learning and illuminating diagnostic reasoning patterns. Significant challenges persist, including marked variability in curriculum design, inadequate time allocation, the theory-to-practice gap, and motivational barriers among students. Assessment frameworks are often poorly aligned with the applied, clinical nature of ECG competency, over-relying on decontextualised recall-based formats. This review synthesises available evidence to propose a framework for more coherent, standardised, and learner-centred ECG education, and identifies research priorities including the development of validated competency benchmarks, the integration of spaced and adaptive learning technologies, and the adoption of interprofessional approaches to cardiac rhythm training.