Electrocardiography (ECG) enhanced by artificial intelligence is emerging as a promising tool for diagnosing and managing cardiovascular disease. This review explains how AI can detect subtle patterns in the ECG that human interpreters may miss, helping to identify problems such as arrhythmias and heart failure more quickly and consistently.
However, the authors also highlight important limitations, including systematic bias. When training datasets do not adequately represent different ages, sexes and racial groups, models can perform worse for those who are underrepresented, since ECG patterns vary with age, physiological differences between the sexes and genetic factors. To make these tools safe and fair in clinical practice, the review calls for more diverse datasets, bias mitigation strategies and continuous validation across populations.
Learn more about this review here: https://doi.org/10.1016/j.jelectrocard.2024.01.006
Reference:
Muzammil, M.A., Javid, S., Afridi, A.K., Siddineni, R., Shahabi, M., Haseeb, M., Fariha, F., Kumar, S., Zaveri, S., & Nashwan, A.J. (2024). Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. Journal of Electrocardiology, 83, 30–4