AI-Powered ECG interpretation: Only trusted, externallyvalidated and approved tools can turn innovation into practice

Artificial Intelligence (AI) has gained rapid attention over the last years in cardiology scientific literature. One promising area of application is ECG automatic interpretation, as ECG signals are, by nature, more prone to be analyzed by machines than by humans [1]. The hopes are about enhanced efficiency-reducing time spent to analyzing visually complex signals-and improving diagnostic accuracy. Yet, despite a vast number of publications showing good performance, AI tools frequently fail to translate into clinical practice. Indeed, clinicians want more than results reported in scientific articles, they need to rely on regulatory-approved medical devices, for example with FDA clearance or CE marking, that they can use confidently in clinical practice. To reach this level of trust, demonstration of good performance through robust external validation is essential. These validations evaluate performance across independent patient populations, devices, and clinical sites not involved in training, ensuring realworld generalizability. Here we discuss why external validation is vital and highlight some recent studies reporting such results with regulatory-approved devices aiming at analyzing ECG recordings.