EKG-Diagnostik mit Hilfe künstlicher Intelligenz: aktueller Stand und zukünftige Perspektiven – Teil 2: Aktuelle Studienlage und Ausblick

While fundamental aspects of the application of artificial intelligence (AI) to electrocardiogram (ECG) analysis were discussed in part 1 of this review, the present work (part 2) provides a review of recent studies on the practical application of this new technology. The number of published articles on the topic of AI-based ECG analysis has been increasing rapidly since 2017. This is especially true for studies that use deep learning (DL) with artificial neural networks. The aim is not only to overcome the weaknesses of classical ECG diagnostics, but also to extend the functionality of the ECG. This involves the detection of cardiological and noncardiological diseases and the prediction for clinical events, e.g., the future development of left ventricular dysfunction and future clinical manifestation of atrial fibrillation. This is made possible by AI using DL to find subclinical patterns in giant ECG datasets and using them for algorithm development. AI-assisted ECG analysis is becoming a screening tool; it goes far beyond just being “better” than a cardiologist. The progress that has been made is remarkable and is generating much attention and also euphoria among experts and the public. However, most studies are proof-of-concept studies. Often, private (institution-owned) data are used, the quality of which is unclear. To date, clinical validation of the developed algorithms in other collectives and scenarios has been rare. Particularly problematic is that the way AI finds a solution so far mostly remains hidden from humans (black-box character of AI). Overall, AI-based electrocardiography is still in its infancy. However, it is already foreseeable that the ECG, as a diagnostic procedure that is easy to use and can be repeated as often as desired, will not only continue to be indispensable in the future, but will also gain in clinical importance.