A systematic review of eye-tracking technology in electrocardiogram interpretation research
Bibliographic record
Abstract
BACKGROUND: Eye-tracking technology provides an objective method for analyzing visual behavior during diagnostic tasks such as electrocardiogram (ECG) interpretation. As a high-stakes and visually complex clinical skill, ECG interpretation benefits from technologies that differentiate experts from novices via gaze patterns. We systematically reviewed eye-tracking in ECG research to examine links between gaze metrics and diagnostic performance and to appraise educational applications. METHODS: Following PRISMA (PROSPERO CRD420251052940), we searched PubMed, Embase, Web of Science, Scopus, ERIC, IEEE Xplore, and PubPsych databases to September 2025. Eligible studies used eye-tracking during ECG interpretation and reported visual behavior or diagnostic outcomes. Two independent reviewers performed the study selection, data extraction, and quality assessment using the Jadad and Newcastle-Ottawa scales. Given heterogeneity, we conducted a narrative synthesis. RESULTS: Nineteen studies (573 participants) were included, mainly involving students or early-career clinicians. Common metrics included fixation duration, counts, time to first fixation, and scan-path efficiency. Experts showed faster, more targeted fixation on critical leads (V1, V2, and II), shorter interpretation times, and higher accuracy. Novices exhibited scattered visual behavior and delayed attention to diagnostically relevant regions. Eye movement data were informative of underlying cognitive processes, including the selection, organization, and integration of ECG features. Additional factors, such as presentation format, individual experience, metacognitive strategies, and emotional responses, may moderate gaze behavior, although these were underexplored. Educational interventions leveraging expert gaze modeling or structured checklists showed mixed but promising outcomes. CONCLUSIONS: Eye-tracking differentiates expertise in ECG interpretation and may inform diagnostic reasoning, but prospective validation is needed. Gaze metrics are candidate indicators of performance and a foundation for feedback-based educational tools. Standardized protocols, larger multi-site studies, and inclusion of diverse learners are required to realize the potential of eye-tracking in ECG training and assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".