[AI-Assisted ECG diagnostics : Classical test statistics still apply]

Artificial intelligence (AI) is revolutionizing electrocardiography with impressive performance metrics. However, these figures can be misleading if classical test statistics-particularly pretest probability-are not considered. Bayes' theorem demonstrates that even excellent AI algorithms can produce numerous false-positive results when disease prevalence is low. Likelihood ratios provide prevalence-independent assessment; calibration and clinical net benefit complement discrimination metrics such as area under the curve (AUC). Physicians must understand the epidemiological context of their patient populations and interpret AI results critically-not noncritically adopt algorithmic decisions.