A Comprehensive Review on Automated Cardiac Report Generation Using Generative AI

Abstract

Cardiovascular diseases (CVDs) have been the major cause of death in all parts of the world, with a tremendous burden on healthcare systems. Accurate and timely clinical reporting is essential to diagnosis, treatment planning, and management of patients. Still, the manual generation of reports is time-consuming, prone to error, and not consistently reflective. The use of Generative Artificial Intelligence (AI) and especially transformer-based Large Language Models (LLMs) and multimodal deep learning models offers transformative potential to automate the process of cardiac report generation and improve it. This review focuses on the current state-of-the-art AI-based methods with a focus on semantic understanding, multimodal understanding, and clinical relevance. EchoGPT, MEIT, and ECG-GPT are developed as advanced models that prove it is possible to create human-like clinical narratives, but note the weaknesses of factual accuracy and practical implementation. We present a comparative study of traditional and modern machine learning techniques, comment on the technical, clinical, and ethical issues, and lay out the strategic priorities of further research to enhance reliability, interpretability, and implementation of automated cardiac reporting systems.