Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs

PRECISE-ECG: Prospective Randomized Evaluation of Clinical Intelligence Support to Reduce Errors in Normal ECGs

This study will evaluate the performance of specialist physicians in interpreting normal electrocardiograms (ECGs) with and without the assistance of an artificial intelligence (AI) neural network. The primary aim is to determine whether AI support affects the rate of false-positive interpretations of normal tracings. Secondary aims include evaluating the time required for interpretation, the sensitivity for detecting abnormalities, and the effect on false positives in ECGs with major abnormalities according to the Minnesota Code system. All ECGs in the sample will be reviewed by a panel of three specialists, to determine the reference classification.

Inclusion Criteria:

• ECGs performed routinely by the Rede de Telemedicina de Minas Gerais (RTMG)

Exclusion Criteria:

• ECGs from patients younger than 18 years