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    Area of Science:

    • Cardiology
    • Medical Informatics
    • Artificial Intelligence

    Background:

    • The medical field generates vast amounts of data, overwhelming physicians' capacity for efficient analysis and utilization.
    • Traditional rule-based expert systems demonstrate limitations in addressing complex medical tasks and extracting insights from big data.
    • Deep learning (DL) presents a powerful alternative, offering enhanced accuracy and effectiveness across various medical applications.

    Purpose of the Study:

    • To review and analyze the application of deep learning techniques in cardiology, utilizing structured data, signal, and imaging modalities.
    • To discuss the inherent advantages and limitations of implementing deep learning in cardiology, with broader implications for general medicine.
    • To identify and propose the most promising directions for the clinical integration of deep learning in medical practice.

    Main Methods:

    • Systematic survey of published research papers focusing on deep learning applications in cardiology.
    • Analysis of studies employing structured data, physiological signals, and medical imaging within cardiology.
    • Evaluation of deep learning's performance, advantages, and limitations in the context of clinical cardiology.

    Main Results:

    • Deep learning models demonstrate significant potential in improving diagnostic accuracy, predictive capabilities, and intervention strategies in cardiology.
    • The layered, nonlinear transformation of data by deep learning effectively reveals hierarchical relationships and complex structures within medical datasets.
    • Identified key areas where deep learning excels and areas requiring further development for robust clinical implementation.

    Conclusions:

    • Deep learning represents a transformative technology for managing and interpreting complex medical data, particularly in cardiology.
    • The review highlights the advantages of deep learning over traditional methods for big data analysis in medicine.
    • Specific, viable clinical applications and future research directions are proposed to facilitate the integration of deep learning into routine medical practice.