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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Large Language Models in Cardiology: Systematic Review.

Moran Gendler¹, Girish N Nadkarni², Karin Sudri³

¹Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold St 8, Safed, Israel, Safed, 1311502, Israel, 972 542354444.

|April 16, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) show promise in cardiology for tasks like ECG interpretation and education. However, limitations in emergency guidance and readability necessitate further research and validation for safe clinical use.

Keywords:

LLMs artificial intelligence cardiology generative AI large language models natural language processing

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Area of Science:

Artificial Intelligence in Medicine
Cardiology Applications
Natural Language Processing

Background:

Large language models (LLMs) are increasingly adopted in healthcare.
Their specific utility and limitations in cardiology remain unevaluated.

Purpose of the Study:

To systematically review the applications and performance of LLMs in cardiology.
To assess LLM capabilities across various cardiology tasks, including chronic conditions, acute events, education, and diagnostics.

Main Methods:

A systematic literature search was performed in PubMed and Scopus.
Studies evaluating LLM performance in cardiology tasks were included and assessed for risk of bias.

Main Results:

LLMs demonstrated high accuracy in heart failure (91%) and ECG interpretation (91%), outperforming physicians in the latter.
Performance varied, with issues in emergency guidance accuracy and readability across different LLM versions and tasks.
ChatGPT-4 showed improved accuracy over ChatGPT-3.5 in physician education tasks.

Conclusions:

LLMs offer significant potential in cardiology, particularly for ECG interpretation and medical education.
Current limitations in emergency scenarios and readability require attention.
Further research with multimodal models and prospective validation is essential.