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Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia.
Guo, Rong-Xin; Tian, Xu; Bazoukis, George; Tse, Gary; Hong, Shenda; Chen, Kang-Yin; Liu, Tong.
Afiliación
  • Guo RX; Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China.
  • Tian X; Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China.
  • Bazoukis G; Department of Cardiology, Larnaca General Hospital, Inomenon Polition Amerikis, Larnaca, Cyprus.
  • Tse G; Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus.
  • Hong S; Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China.
  • Chen KY; Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China.
  • Liu T; School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China.
Pacing Clin Electrophysiol ; 47(6): 789-801, 2024 06.
Article en En | MEDLINE | ID: mdl-38712484
ABSTRACT
The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Arritmias Cardíacas / Inteligencia Artificial Idioma: En Revista: Pacing Clin Electrophysiol Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Arritmias Cardíacas / Inteligencia Artificial Idioma: En Revista: Pacing Clin Electrophysiol Año: 2024 Tipo del documento: Article