The Impact of Artificial Intelligence on Arrhythmia Detection in Intensive Care Units: A Review Study
Code: G-1371
Authors: شهره جوادپور, Saeedeh Eskandari * ℗, سارا مقدم
Schedule: Not Scheduled!
Tag: Clinical Decision Support System
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Abstract:
Abstract
Background and aims: Cardiac arrhythmias account for a significant portion of sudden deaths, especially among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care. However, in the healthcare and medical field, accurately diagnosing many conditions with complex and overlapping symptoms—including cardiac arrhythmias—remains a challenge. Since artificial intelligence (AI) offers various solutions to a wide range of challenges, it can be used to process massive amounts of patient data and identify disease patterns more efficiently than traditional time-consuming methods. Method: The present study is a narrative review conducted using the (or_ and) strategy across both Persian and English databases (Medline, Cochrane, PubMed, Scopus, SID, ISC, Magiran), with a time limitation from 2018 to 2024. It searched for Persian and English articles related to the topic. This study followed five stages proposed by Knafl & Whittemore: identifying and setting objectives, searching for sources, data evaluation, data analysis, and presenting results. Results: After applying the inclusion criteria and evaluating the articles, 18 out of 26 articles were finally included in the study. The articles were screened by researchers independently and simultaneously in three stages (title, abstract, full text). Ultimately, 8 studies closely aligned with the research objectives were selected. The relevant data indicates that with the evolution of AI tools, the diagnosis and prognosis of cardiac arrhythmias have become easier than before. Conclusion: The results showed that AI-based techniques for diagnosing cardiac arrhythmias can assist healthcare professionals in accurately identifying arrhythmias, making early screening easier and more precise.
Keywords
Artificial Intelligence, Arrhythmia, Intensive Care Units
