Work

Advances in Electrocardiogram-Based Artificial Intelligence Reveal Multisystem Biomarkers.

Year: 2025

Type: article

Abstract: As Artificial Intelligence (AI) plays an increasingly prominent role in society, its application in clinical cardiology is gaining traction by providing innovative diagnostic, prognostic, and therapeutic solutions. Electrocardiogram (ECG), as a ubiquitous diagnostic tool in cardiology, has emerged as the leading data source for Deep Learning (DL) applications. A recent study from our group used ECG-based DL model to identify cardiac wall motion abnormalities and outperformed expert human interpretation. Motivated by this work and that of many others, we aim to discuss advances, limitations, future directions, and equity considerations in DL models for ECG-based AI applications.

Source: PubMed

Authors: Xichong Liu, Sabyasachi Bandyopadhyay, Albert J Rogers

Institutions: Stanford Medicine, Stanford Cardiovascular Institute, Stanford University

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institutes of Health

Award: Electrical Mapping Signatures of Adverse Structural and Functional Remodeling in Ventricular Arrhythmia