ARTIFICIAL INTELLIGENCE IN EARLY DETECTION OF MYOCARDIAL INFARCTION USING WEARABLE DEVICES: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Myocardial infarction (MI) remains a leading global cause of morbidity and mortality, with early detection being critical for improving clinical outcomes. Conventional diagnostic methods often rely on patient presentation to healthcare settings, leading to delayed intervention. Wearable devices integrated with artificial intelligence (AI) offer the potential for real-time, non-invasive, and accessible MI detection. However, there is limited consolidated evidence evaluating the diagnostic accuracy and clinical utility of these technologies. Objective: This systematic review aims to evaluate the effectiveness and diagnostic performance of AI-driven wearable devices in the early detection of myocardial infarction. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Literature was searched across PubMed, Scopus, Web of Science, and IEEE Xplore for studies published between 2019 and 2024. Included studies evaluated adult human subjects using AI-integrated wearable or portable ECG devices for MI detection. Study designs encompassed randomized trials, cohort studies, and validation models. Risk of bias was assessed using the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale. Data were synthesized narratively due to heterogeneity in study design and outcomes. Results: Eight studies were included in the final review. AI algorithms demonstrated high diagnostic accuracy (sensitivity and specificity >90%) across multiple device platforms. Notably, one study reported an AUC of 0.9954 and another achieved an F-score of 88.10%. AI models were successfully integrated into real-time or embedded systems, and performance was comparable or superior to clinician-based interpretations. Study quality was moderate to high, although variations in design and small sample sizes were noted. Conclusion: AI-enhanced wearable technologies show strong promise for the early detection of myocardial infarction, offering significant clinical benefits in timely diagnosis and intervention. While the current evidence supports their diagnostic value, further large-scale prospective trials are needed to validate performance and guide clinical implementation.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".