Advancements in Artificial Intelligence for Cardiovascular Disease Diagnosis and Risk Stratification

Abstract

The article analyses the use of artificial intelligence (AI) in the different branches of cardiology including its use in predictive judgement, diagnostics, and risk evaluation. It examines narrow (applied) AI and general (strong) AI with special attention to the machine learning methods applied to data from ECG, echocardiography, sonography, CT, MRI, and PET scans. It also attempts to show how AI can improve diagnostic accuracy, minimize medical blunders, and improve AI-assisted treatment planning. It demonstrates how AI can be used for the patient telephone calls in emergency medical services and, as part of preventive cardiology, for the analysis of enormous databases received from tonometry, pulse wave velocity, and biochemical investigations. Attention is also given to the issues concerning AI integration, such as using LLMs for clinical documentation. Further, ethical issues relating to AI including data privacy and culpability for the actions of AI are discussed. Further, it has been highlighted that the considered aspect needs more attention, therefore, patient data security and AI tools integration in medical practice requires precise regulation to ensure that facilitated patient outcomes do not come at the cost of patient data breach. Necessary measures for successful integration of AI tools into practice of cardiology directed to improvement of patient care quality and healthcare systems effectiveness are given in conclusion.

Keywords: Artificial Intelligence, Machine Learning, Cardiovascular Diseases, Predictive Analytics, Diagnostic Tools, Risk Stratification, Electrocardiography, Echocardiography, MRI, CT, Large Language Models, Healthcare Integration, Ethical Issues.