Cardi-GPT: An Expert ECG-Record Processing Chatbot

TL;DR

Cardi-GPT is an AI-powered chatbot that interprets ECG data using deep learning, providing accurate, clinically meaningful insights to improve cardiovascular diagnosis and communication in diverse healthcare settings.

Contribution

This paper introduces Cardi-GPT, a novel system combining CNN-based ECG analysis with natural language interaction to enhance clinical interpretation and communication.

Findings

Achieved 0.6194 weighted accuracy across 24 cardiac conditions.

Demonstrated superior performance over baseline models on multi-hospital datasets.

Attained 73% response quality score in clinical communication evaluation.

Abstract

Interpreting and communicating electrocardiogram (ECG) findings are crucial yet challenging tasks in cardiovascular diagnosis, traditionally requiring significant expertise and precise clinical communication. This paper introduces Cardi-GPT, an advanced expert system designed to streamline ECG interpretation and enhance clinical communication through deep learning and natural language interaction. Cardi-GPT employs a 16-residual-block convolutional neural network (CNN) to process 12-lead ECG data, achieving a weighted accuracy of 0.6194 across 24 cardiac conditions. A novel fuzzification layer converts complex numerical outputs into clinically meaningful linguistic categories, while an integrated chatbot interface facilitates intuitive exploration of diagnostic insights and seamless communication between healthcare providers. The system was evaluated on a diverse dataset spanning six hospitals across four countries, demonstrating superior performance compared to baseline models. Additionally, Cardi-GPT achieved an impressive overall response quality score of 73\%, assessed using a comprehensive evaluation framework that measures coverage, grounding, and coherence. By bridging the gap between intricate ECG data interpretation and actionable clinical insights, Cardi-GPT represents a transformative innovation in cardiovascular healthcare, promising to improve diagnostic accuracy, clinical workflows, and patient outcomes across diverse medical settings.