  Automated interpretation of electrocardiograms (ECG) has garnered significant
attention with the advancements in machine learning methodologies. Despite the
growing interest, most current studies focus solely on classification or
regression tasks, which overlook a crucial aspect of clinical cardio-disease
diagnosis: the diagnostic report generated by experienced human clinicians. In
this paper, we introduce a novel approach to ECG interpretation, leveraging
recent breakthroughs in Large Language Models (LLMs) and Vision-Transformer
(ViT) models. Rather than treating ECG diagnosis as a classification or
regression task, we propose an alternative method of automatically identifying
the most similar clinical cases based on the input ECG data. Also, since
interpreting ECG as images is more affordable and accessible, we process ECG as
encoded images and adopt a vision-language learning paradigm to jointly learn
vision-language alignment between encoded ECG images and ECG diagnosis reports.
Encoding ECG into images can result in an efficient ECG retrieval system, which
will be highly practical and useful in clinical applications. More importantly,
our findings could serve as a crucial resource for providing diagnostic
services in underdeveloped regions.
