Artificial Intelligence vs. Human Expertise in Diagnosing Acute Coronary Syndromes: A Meta-Analysis Focusing on 12-Lead Electrocardiogram Model
Bibliographic record
Abstract
Background: Machine Learning (ML) is a branch of artificial intelligence (AI) that uses algorithms to produce models capable of performing complex tasks. Diagnosing acute coronary syndromes (ACS) without myocardial damage is still subjective and relies heavily on the clinical expertise of healthcare professionals to interpret electrocardiograms (ECG). Artificial intelligence can help accelerate the diagnosis of ACS, prompting early intervention and better clinical outcomes. Objective: This study evaluates the diagnostic accuracy of ML-based AI versus healthcare professionals in identifying ACS from 12-lead ECGs. Method: A comprehensive search was done on electronic databases, including PubMed, Embase, MEDLINE, and Science Direct. Keywords used include artificial intelligence, electrocardiography, acute coronary syndrome, and diagnostic accuracy. All eligible studies were assessed using the New Castle Ottawa Scale (NOS). A meta-analysis on diagnostic test accuracy using Review Manager (RevMan) Version 5.4 was conducted. Result: A total of 8 studies with fair or good quality were included, encompassing 22.731 ACS patients. Overall, the application of machine learning shows superiority over healthcare expertise, especially regarding sensitivity. The sensitivity range of artificial intelligence usage ranged from 0.75–0.98, while that of healthcare professionals ranged from 0.50–0.77. For specificity, the range was 0.91–1.00 and 0.85–0.96 for AI and healthcare professional expertise, respectively. Conclusion: The results of recent evidence demonstrated the superiority of AI in detecting subtle ischemic ECG changes indicative of ACS, offering independence from observer variability. Continued refinement of AI models could enhance diagnostic reliability and patient outcomes. Keywords: Artificial intelligence; human expertise; electrocardiogram
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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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.055 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".