Implementation of an AI-Based Diagnostic Management System for Rapid Detection of Cardiovascular Disease

Debajyoti Chatterjee, Surajit Sur, Rahul Kumar Garg

2024

Abstract

Cardiovascular disease remains a leading cause of global mortality, necessitating advancements in early detection and prevention methods. This study investigates the application of artificial intelligence (AI) in enhancing the accuracy and speed of cardiovascular disease diagnostics, with a specific focus on preventing heart attacks. Reviewing existing AI models and their integration into clinical workflows, we identify significant improvements in diagnostic precision and patient outcomes. Our findings highlight AI technologies like AliveCor, KardiaMobile, HeartFlow, FFRct, and Viz.ai, which demonstrate superior accuracy, sensitivity, and specificity compared to traditional methods. Despite these advancements, challenges in seamless integration, data privacy, ethical considerations, and regulatory compliance persist. We propose a comprehensive strategy to address these barriers, emphasizing the need for longitudinal studies, diverse population validation, and the development of ethical frameworks. The successful implementation of AI in cardiology holds promise for reducing the global burden of cardiovascular diseases, yielding substantial health, social, and economic benefits.

Paper Citation

in Harvard Style

Chatterjee D., Sur S. and Garg R. (2024). Implementation of an AI-Based Diagnostic Management System for Rapid Detection of Cardiovascular Disease. In Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com; ISBN 978-989-758-739-9, SciTePress, pages 135-143. DOI: 10.5220/0013253500004646

in Bibtex Style

@conference{ic3com24,
author={Debajyoti Chatterjee and Surajit Sur and Rahul Garg},
title={Implementation of an AI-Based Diagnostic Management System for Rapid Detection of Cardiovascular Disease},
booktitle={Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com},
year={2024},
pages={135-143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013253500004646},
isbn={978-989-758-739-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Cognitive & Cloud Computing - Volume 1: IC3Com
TI - Implementation of an AI-Based Diagnostic Management System for Rapid Detection of Cardiovascular Disease
SN - 978-989-758-739-9
AU - Chatterjee D.
AU - Sur S.
AU - Garg R.
PY - 2024
SP - 135
EP - 143
DO - 10.5220/0013253500004646
PB - SciTePress