ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA V.3.2

Electrocardiography (ECG) is one of the most widely used diagnostic tools for detecting cardiovascular diseases. ECG recordings capture the electrical activity of the heart and help doctors identify conditions such as arrhythmias, myocardial infarction, heart failure, and conduction abnormalities. However, interpreting ECG signals accurately requires specialized training and experience, and misinterpretation can lead to delayed diagnosis or incorrect treatment.

Artificial Intelligence has shown strong potential in improving ECG analysis by automatically identifying patterns associated with cardiac abnormalities. In this use case, advanced AI models such as multimodal large language models (LLMs) and deep learning architectures are used to interpret ECG images and signals. Techniques such as Parameter-Efficient LoRA (Low-Rank Adaptation) fine-tuning allow these models to adapt to medical tasks while using fewer computational resources.

For additional context and detailed documentation of this use case, please refer to pages 51-56 in the attached Casebook.