Journal article
Can Artificial Intelligence Enhance Syncope Management?
JACC: Advances, Vol.2(3), p.100323
05/2023
DOI: 10.1016/j.jacadv.2023.100323
PMCID: PMC11198330
PMID: 38939607
Abstract
Syncope, a form of transient loss of consciousness, remains a complex medical condition for which adverse cardiovascular outcomes, including death, are of major concern but rarely occur. Current risk stratification algorithms have not completely delineated which patients benefit from hospitalization and specific interventions. Patients are often admitted unnecessarily and at high cost. Artificial intelligence (AI) and machine learning may help define the transient loss of consciousness event, diagnose the cause, assess short- and long-term risks, predict recurrence, and determine need for hospitalization and therapeutic intervention; however, several challenges remain, including medicolegal and ethical concerns. This collaborative statement, from a multidisciplinary group of clinicians, investigators, and scientists, focuses on the potential role of AI in syncope management with a goal to inspire creation of AI-derived clinical decision support tools that may improve patient outcomes, streamline diagnostics, and reduce health-care costs.
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•Syncope remains a complex, difficult to manage condition associated with adverse cardiovascular outcomes.•Artificial intelligence may assist in diagnosis, risk stratification, and management decisions, yet challenges remain.•Prospective, multicenter, and multidisciplinary datasets could serve as the ideal machine learning platform.•Further studies should compare machine learning approaches to existing risk stratification tools and clinical judgment.
Details
- Title: Subtitle
- Can Artificial Intelligence Enhance Syncope Management?
- Creators
- Giselle M. Statz - University of IowaAron Z. Evans - Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USASamuel L. Johnston - University of IowaMehul Adhaduk - University of IowaAvinash R. Mudireddy - The Iowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, Iowa, USAMilan Sonka - The Iowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, Iowa, USASangil Lee - University of IowaE. John Barsotti - Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, USAFabrizio Ricci - Giesecke and Devrient (Germany)Franca Dipaola - Humanitas UniversityMadeleine Johansson - Department of Cardiology, Skåne University Hospital, Lund University, Malmo, SwedenRobert S. Sheldon - University of CalgaryVenkatesh Thiruganasambandamoorthy - University of OttawaRose-Anne Kenny - Trinity College DublinTyler C. Bullis - Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USADeepak K. Pasupula - Division of Cardiovascular Disease, Department of Internal Medicine, MercyOne North Iowa Heart Center, Mason City, Iowa, USAJon Van Heukelom - Department of Emergency Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USAMilena A. Gebska - University of IowaBrian Olshansky - Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
- Resource Type
- Journal article
- Publication Details
- JACC: Advances, Vol.2(3), p.100323
- DOI
- 10.1016/j.jacadv.2023.100323
- PMID
- 38939607
- PMCID
- PMC11198330
- NLM abbreviation
- JACC Adv
- ISSN
- 2772-963X
- Publisher
- Elsevier Inc
- Language
- English
- Date published
- 05/2023
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Engineering Administration; Emergency Medicine; Cardiovascular Medicine; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; General Internal Medicine; Internal Medicine; Ophthalmology and Visual Sciences
- Record Identifier
- 9984413080202771
Metrics
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