# Cardi-GPT: An Expert ECG-Record Processing Chatbot

arXiv: https://arxiv.org/abs/2510.24737 · HTML: https://mlsanity.com/paper/2510.24737/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses](https://mlsanity.com/paper/2006.01413/) (2020)
- [Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram](https://mlsanity.com/paper/2010.10328/) (2020)
- [Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification](https://mlsanity.com/paper/2004.05399/) (2020)
- [Novel split quality measures for stratified multilabel Cross Validation with application to large and sparse gene ontology datasets](https://mlsanity.com/paper/2109.01425/) (2021)
- [Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks](https://mlsanity.com/paper/2210.14253/) (2022)

## Abstract (arXiv)

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.