Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation
AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applicatio...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Radcliffe Medical Media
2023-04-01
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| Series: | Arrhythmia & Electrophysiology Review |
| Online Access: | https://www.aerjournal.com/articleindex/aer.2022.31 |
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| _version_ | 1849220225005780992 |
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| author | David M Harmon Ojasav Sehrawat Maren Maanja John Wight Peter A Noseworthy |
| author_facet | David M Harmon Ojasav Sehrawat Maren Maanja John Wight Peter A Noseworthy |
| author_sort | David M Harmon |
| collection | DOAJ |
| description | AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine. |
| format | Article |
| id | doaj-art-12901794897d43c6b7405750738afefc |
| institution | Kabale University |
| issn | 2050-3369 2050-3377 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Radcliffe Medical Media |
| record_format | Article |
| series | Arrhythmia & Electrophysiology Review |
| spelling | doaj-art-12901794897d43c6b7405750738afefc2024-12-14T16:04:15ZengRadcliffe Medical MediaArrhythmia & Electrophysiology Review2050-33692050-33772023-04-011210.15420/aer.2022.31Artificial Intelligence for the Detection and Treatment of Atrial FibrillationDavid M Harmon0Ojasav Sehrawat1Maren Maanja2John Wight3Peter A Noseworthy4Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US; Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, SwedenDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USAF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.https://www.aerjournal.com/articleindex/aer.2022.31 |
| spellingShingle | David M Harmon Ojasav Sehrawat Maren Maanja John Wight Peter A Noseworthy Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation Arrhythmia & Electrophysiology Review |
| title | Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation |
| title_full | Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation |
| title_fullStr | Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation |
| title_full_unstemmed | Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation |
| title_short | Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation |
| title_sort | artificial intelligence for the detection and treatment of atrial fibrillation |
| url | https://www.aerjournal.com/articleindex/aer.2022.31 |
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