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Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.
Doolub, Gemina; Khurshid, Shaan; Theriault-Lauzier, Pascal; Nolin Lapalme, Alexis; Tastet, Olivier; So, Derek; Labrecque Langlais, Elodie; Cobin, Denis; Avram, Robert.
  • Doolub G; Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada.
  • Khurshid S; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Theriault-Lauzier P; Division of Cardiovascular Medicine, Stanford School of Medicine, Palo Alto, California, USA.
  • Nolin Lapalme A; Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada; Mila-Québec AI Institute, Montréal, Québec, Canada.
  • Tastet O; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada.
  • So D; University of Ottawa, Heart Institute, Ottawa, Ontario, Canada.
  • Labrecque Langlais E; Polytechnique Montréal, Montréal, Québec, Canada.
  • Cobin D; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada.
  • Avram R; Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Québec, Canada; Heartwise (heartwise.ai), Montréal Heart Institute, Montréal, Québec, Canada. Electronic address: robert.avram.md@gmail.com.
Can J Cardiol ; 2024 Jun 18.
Article en En | MEDLINE | ID: mdl-38901544
ABSTRACT
This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2024 Tipo del documento: Article