Can Artificial Intelligence Enhance Syncope Management?

• Published in: JACC. Advances (Online) Vol. 2; no. 3; p. 100323
• Main Authors: Statz, Giselle M., Evans, Aron Z., Johnston, Samuel L., Adhaduk, Mehul, Mudireddy, Avinash R., Sonka, Milan, Lee, Sangil, Barsotti, E. John, Ricci, Fabrizio, Dipaola, Franca, Johansson, Madeleine, Sheldon, Robert S., Thiruganasambandamoorthy, Venkatesh, Kenny, Rose-Anne, Bullis, Tyler C., Pasupula, Deepak K., Van Heukelom, Jon, Gebska, Milena A., Olshansky, Brian
• Format: Journal Article
• Language: English
• Published: Elsevier 01.05.2023
• Subjects: State-of-the-Art Review
• ISSN: 2772-963X; 2772-963X
• DOI: 10.1016/j.jacadv.2023.100323

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. • 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.