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Task-specific versus general-purpose AI models in ECG analysis: A comparative study with emergency medicine
Ertugrul Altinbilek1, Adem Az2, Ozgur Sogut2
1University of Health Sciences, Şişli Hamidiye Etfal Training and Research Hospital, Department of Emergency Medicine, Istanbul, Turkey.
Purpose:
To evaluate and compare the diagnostic accuracy of three Artificial intelligence (AI) models-GPT-4o, Canva-GPT, and ECG Reader-GPT-against emergency medicine specialists (EMSs) in electrocardiogram (ECG) interpretation using a standardized and validated test set.
Methods:
In this prospective diagnostic accuracy study, 50 ECG questions were selected from the reference text 150 ECG Cases. Thirty EMSs completed the test once; each AI model was evaluated on the same test set daily over 30 consecutive days. Diagnostic accuracy was compared across predefined ECG subcategories and clinical case types.
Results:
EMSs achieved the highest overall diagnostic accuracy (median: 41.5; IQR: 37.0-43.0), followed closely by ECG Reader-GPT (median: 39.5; IQR: 39.0-41.0), with no statistically significant difference between them (p = 0.530). ECG Reader-GPT significantly outperformed both GPT-4o and Canva-GPT across all case categories (p < 0.001). Subgroup analysis revealed that ECG Reader-GPT performed comparably to EMSs in identifying ischemic syndromes, channelopathies and genetic syndromes, and normal ECGs (all p < 0.05); it surpassed them in interpreting rhythm disorders (p = 0.007).
Conclusion:
ECG Reader-GPT, a customized AI model for ECG interpretation, demonstrated diagnostic accuracy comparable to experienced EMSs and significantly outperformed general-purpose models across all ECG subcategories. These findings highlight the value of domain specialization in developing clinically effective AI tools.
