{
  "abstract": "Cardiac telemetry has evolved into a vital tool for continuous cardiac monitoring and early detection of cardiac abnormalities. In recent years, artificial intelligence (AI) has become increasingly integrated into cardiac telemetry, making a shift from traditional statistical machine learning models to more advanced deep neural networks. These modern AI models have demonstrated superior accuracy and the ability to detect complex patterns in telemetry data, enhancing real-time monitoring, predictive analytics and personalised cardiac care. In our review, we examine the current state of AI in cardiac telemetry, focusing on deep learning techniques, their clinical applications, the challenges and limitations faced by these models, and potential future directions in this promising field.",
  "authors": [
    {
      "affiliations": [
        "Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, USA"
      ],
      "name": "Jiaying Lu"
    },
    {
      "affiliations": [
        "Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, USA"
      ],
      "name": "Ran Xiao"
    },
    {
      "affiliations": [
        "Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, USA",
        "Wallace H Coulter Department of Biomedical Engineering, GeorgiaInstitute of Technology & Emory University, Atlanta, Georgia, USA"
      ],
      "name": "Xiao Hu"
    },
    {
      "affiliations": [
        "UCLA Cardiac Arrhythmia Center, Los Angeles, California, USA"
      ],
      "name": "Duc H Do"
    }
  ],
  "title": "Artificial intelligence in cardiac telemetry",
  "uid": "fba4b369-28a2-54f2-8436-041f72eff3a2"
}