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The text emphasizes clinical use cases including improved medical image analysis and machine-learning–based prediction tasks for cardiovascular outcomes. Key barriers to broader development are identified as limited diversity of data and restricted access to large datasets. 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Artificial intelligence tools and technology are utilized for the early identification and diagnosis of severe or intricate heart conditions. The potential of artificial intelligence (AI) in cardiovascular medicine is significant. However, the major challenges hindering the development of AI applications in this field include the scarcity of diverse data and limited availability of huge datasets. These revolutionary digital technologies will play a significant role in shaping the future of cardiology.\u003Cbr>Key Words: Artificial intelligence, machine learning, cardiovascular disease |  | ÖZ\u003Cbr>Yapay zeka (AI) ve makine öğrenimi (ML), tıp da dahil olmak üzere modern yaşamınneredeyse her alanına dokunan bir zekadır. Yapay zeka araçları ve teknolojileri, ciddiveya karmaşık kalp sorunlarının erken risk tahmini ve teşhisine uygulanmaktadır. Kardiyovasküler tıpta yapay zekanın potansiyeli büyüktür, ancak sınırlı veri çeşitliliği ve birçok büyük veri tabanına erişim eksikliği, yapay zeka uygulamalarınıngeliştirilmesinin önündeki en büyük engeller arasındadır. Kardiyolojinin geleceğibüyük ölçüde bu yenilikçi dijital teknolojilere dayanacaktır.\u003Cbr>Anahtar Sözcükler: Yapay zeka, makine öğrenimi, kardiyovasküler hastalıklar |\n| --- | --- | --- |\n\nReceIved: 22.08.2024 Accepted: 23.08.2024 publIshed (OnlIne): 30.08.2024  \n* Corresponding Author: Ahmet Aslan, MD, Medical School of Alanya Alaaddin Keykubat University, Department of Orthopedics and Traumatology, Alanya/Antalya, Türkiye. Phone: 05056462411 / mail: [ahmet.aslan@alanya.edu.tr](ahmet.aslan@alanya.edu.tr)  \nORCID: 0000-0001-5797-1287  \nTo cited: Öncel CR, Aslan A. The potential of artificial intelligence and machine learning algorithms in the field of cardiovascular diseases Acta Med. Alanya 2024;8(2): 91-92 doi: 10.30565/medalanya.1537200  \nArtificial intelligence (AI) is intelligence that  \naffects nearly every aspect of modern life, including medicine. Clinical practice increasingly employs artificial intelligence tools and technology to assist individuals suffering from or at risk of developing heart disease. We use these novel techniques to detect and diagnose serious or complex heart diseases early. Furthermore, AI has the ability to improve medical image analysis, such as echocardiography and cardiac MRI scans . Clinical data like electrocardiograms (ECGs), echocardiograms, and medical imaging can apply  \nmachine learning algorithms to predict outcomes, stratify risks, and identify cardiovascular diseases [1] .  \nOne of the most common uses of machine learning (ML) in cardiology is to predict heart rhythm abnormalities. The most common clinically significant arrhythmia, atrial fibrillation (AF) carries a substantial risk of stroke, heart failure and death. Therefore, significant efforts are currently underway to enhance AF screening, early identification and treatment. Among these initiatives, AI technology  \nActa Medica Alanya MAY -AUG 2024 Open Access [http://dergipark.gov.tr/medalanya.](http://dergipark.gov.tr/medalanya. 91)[ 91](http://dergipark.gov.tr/medalanya. 91)  \nThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License.  \nhas shown therapeutic util","cbCaipSx1IpRxFoo","https://ap.wps.com/l/cbCaipSx1IpRxFoo","pdf",428839,"English","# Abstract\n## Applications in cardiovascular diagnosis and monitoring\n## Common machine learning uses and clinical impact\n## Outcomes, risk prediction, and future prospects","[{\"question\":\"How do AI and ML support early diagnosis of cardiovascular diseases?\",\"answer\":\"AI tools and ML approaches are described as enabling early identification and diagnosis of severe or intricate heart conditions through clinical data and predictive modeling.\"},{\"question\":\"What main challenges limit the development of AI applications in cardiology?\",\"answer\":\"The document points to the scarcity of diverse datasets and limited access to huge datasets as major obstacles.\"},{\"question\":\"Which clinical scenarios does the text identify as important for AI/ML use?\",\"answer\":\"It highlights arrhythmia screening (especially atrial fibrillation), heart failure prediction and remote monitoring, and risk assessment related to cardiovascular disease outcomes, including coronary artery disease imaging analysis.\"}]","The potential of artificial intelligence and machine learning algorithms in the field of cardiovascular diseases - Editorial | PDF"]