Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis
Year: 2022
Type: review
Abstract: Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.
Authors: Cheuk To Chung, Sharen Lee, Emma King, Tong Liu, Antonis A. Armoundas, George Bazoukis, Gary Tse
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Awards: A Medical-Grade Smart-Phone Based Monitoring System, Utility of Vagal Stimulation to Prevent the Onset of Ventricular Arrhythmias, G5639061294, G6168824270, Prevention and Treatment of Ventricular Tachyarrhythmias, G7909210140, Predicting In-Hospital Cardiac Arrest Using Machine Learning and Massive Streaming Electronic Patient Data
OpenAlex ID: W4298119164