Autoencoders in cardiology: Opportunities and challenges for clinical integration

The increasing availability of high-dimensional cardiology data—ranging from electrocardiograms (ECGs) and echocardiograms to cardiac MRI and wearable device outputs—presents both opportunities and challenges for clinical insight and prediction. Autoencoders (AEs), a class of unsupervised deep learning models, have emerged as powerful tools for learning compact, informative representations of such complex datasets. By compressing and reconstructing input data, AEs can uncover latent features that capture the essential characteristics of cardiac function and pathology. This capability has enabled applications including anomaly detection in ECG signals, denoising of imaging data, patient stratification, and early diagnosis of cardiovascular diseases. Variants, such as variational AEs and convolutional AEs, further enhance performance by modeling uncertainty and leveraging spatial structure. Despite promising results, challenges remain in interpretability, clinical integration, and generalization across diverse patient populations. This chapter reviews recent developments in the application of AEs in cardiology, highlighting their potential to contribute to personalized medicine and improve clinical outcomes.