Pre-trained artificial intelligence models in the prediction and classification of myocardial infarction

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dc.contributor.authorÇolak, Cemil
dc.contributor.authorArslan, Ahmet Kadir
dc.contributor.authorYağın, Fatma Hilal
dc.contributor.authorPınar, Abdulvahap
dc.date.accessioned2026-06-19T06:29:53Z
dc.date.available2026-06-19T06:29:53Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThis work aims to provide a thorough examination of the existing research on the use of pre-trained artificial intelligence models for predicting and categorizing myocardial infarction, emphasizing the various models, methodologies, and findings documented in the literature. This research is a literature review examining the use of pre-trained artificial intelligence models for myocardial infarction prediction and classification. We utilized research publications from academic sources dealing with myocardial infarction and artificial intelligence applications. The technique covered artificial intelligence models (particularly BERT, ResNet, and XGBoost), data sources (EHR, ECG, and CMR), methods (transfer learning, deep learning, and machine learning), and the quality of the literature review was evaluated using the Scale for the Assessment of Narrative Review Articles (SANRA). Pre-trained artificial intelligence models, particularly CNN and transformer architectures, have significant promise in the prediction and categorization of myocardial infarction. These models provide elevated accuracy, prompt detection, and tailored methodologies, while optimizing data and computing resource use. This research thoroughly studied the use of pre-trained artificial intelligence models to predict and classify myocardial infarction. Our findings indicate that models based on CNN and transformer topologies, in particular, provide considerable benefits in the diagnosis and treatment of myocardial infarction, with the possibility for early detection and a tailored strategy. However, concerns such as data quality, model interpretability, and the requirement for thorough validation must be addressed in clinical practice. Future research addressing these constraints and concentrating on the practical and ethical implementation of AI-based solutions in cardiology has the potential to enhance patient outcomes and usher in a new age of precision medicine. This review was created using the Scale for the Assessment of Narrative Review Articles (SANRA) criteria, which included the topic's relevance, defined goals, a literature search, and acceptable source citing.
dc.identifier.doi10.5455/medscience.2025.07.199
dc.identifier.endpage563
dc.identifier.issn2147-0634
dc.identifier.issue1
dc.identifier.startpage554
dc.identifier.trdizinid1391581
dc.identifier.urihttps://doi.org/10.5455/medscience.2025.07.199
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1391581
dc.identifier.urihttps://hdl.handle.net/20.500.12899/4810
dc.identifier.volume15
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofMedicine Science
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20260612
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectKalp Ve Kalp Damar Sistemi
dc.subjectBilgisayar Bilimleri
dc.subjectYapay Zeka
dc.titlePre-trained artificial intelligence models in the prediction and classification of myocardial infarction
dc.typeArticle

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