The Hope and the Hype of Artificial Intelligence for Syncope Management

Samuel L Johnston; E John Barsotti; Constantinos Bakogiannis; Artur Fedorowski; Fabrizio Ricci; Eric G Heller; Robert S Sheldon; Richard Sutton; Win-Kuang Shen; Venkatesh Thiruganasambandamoorthy; Mehul Adhaduk; William H Parker; Arwa Aburizik; Corey R Haselton; Alex J Cuskey; Sangil Lee; Madeleine Johansson; Donald Macfarlane; Paari Dominic; Haruhiko Abe; B Hygriv Rao; Avinash Mudireddy; Milan Sonka; Roopinder K Sandhu; Rose Anne Kenny; Giselle M Statz; Rakesh Gopinathannair; David Benditt; Franca Dipaola; Mauro Gatti; Roberto Menè; Alessandro Giaj Levra; Dana Shiffer; Giorgio Costantino; Raffaello Furlan; Martin H Ruwald; Vassilios Vassilikos; Milena A Gebska; Brian Olshansky

doi:10.1093/ehjdh/ztaf061

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The Hope and the Hype of Artificial Intelligence for Syncope Management

Journal article

Open access

Peer reviewed

The Hope and the Hype of Artificial Intelligence for Syncope Management

Samuel L Johnston, E John Barsotti, Constantinos Bakogiannis, Artur Fedorowski, Fabrizio Ricci, Eric G Heller, Robert S Sheldon, Richard Sutton, Win-Kuang Shen, Venkatesh Thiruganasambandamoorthy, …

European heart journal. Digital health, Vol.6(5), pp.1046-1054

09/22/2025

DOI: 10.1093/ehjdh/ztaf061

PMCID: PMC12450521

PMID: 40984999

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Abstract

Aims Syncope remains a diagnostic challenge despite advancements in testing and treatment. Cardiac syncope is an independent predictor of mortality and can be difficult to distinguish from other causes of transient loss of consciousness (TLOC). This paper explores whether artificial intelligence (AI) can improve the evaluation and management of patients with syncope. Methods and results We conducted a literature review and incorporated the opinions of experts in the fields of syncope and AI. The cause of TLOC is often unclear, hospitalization criteria are ambiguous, diagnostic tests are frequently non-informative, and assessments are costly. Patients are left with unanswered questions and limited guidance. Artificial intelligence (AI) has the potential to optimize syncope evaluation by processing large data sets, detecting imperceptible patterns, and assisting clinicians. However, AI has limitations, including errors, lack of human empathy, and uncertain clinical utility. Liability issues further complicate its integration. We present three viewpoints: (i) AI is crucial for advancing syncope management; (ii) AI can enhance the patient experience; and (iii) AI in syncope care is inevitable. Conclusion Artificial intelligence may improve syncope diagnosis and management, particularly through machine learning–based test interpretation and wearable device data. However, it has yet to surpass human clinical judgment in complex decision-making. Current challenges include gaps in understanding syncope mechanisms, AI interpretability, generalizability, and clinical integration. Standardized diagnostic approaches, real-world validation, and curated data sets are essential for progress. Artificial intelligence may enhance efficiency and communication but raises concerns regarding confidentiality, bias, inequities, and legal implications.

Syncope

Artificial intelligence

Hope

Hype

Clinical management

Patient experience

Details

Title: Subtitle: The Hope and the Hype of Artificial Intelligence for Syncope Management
Creators: Samuel L Johnston - University of Iowa
E John Barsotti - University of Iowa
Constantinos Bakogiannis - Aristotle University of Thessaloniki
Artur Fedorowski - Karolinska University Hospital
Fabrizio Ricci
Eric G Heller - University of Iowa
Robert S Sheldon - University of Calgary
Richard Sutton - Hammersmith Hospital
Win-Kuang Shen - Mayo Clinic Comprehensive Cancer Center (Arizona)
Venkatesh Thiruganasambandamoorthy - University of Ottawa
Mehul Adhaduk - University of Iowa
William H Parker - University of Iowa
Arwa Aburizik - University of Iowa
Corey R Haselton - University of Iowa
Alex J Cuskey - University of Iowa
Sangil Lee - University of Iowa
Madeleine Johansson - Skåne University Hospital
Donald Macfarlane - University of Iowa
Paari Dominic - University of Iowa
Haruhiko Abe - University of Occupational and Environmental Health Japan
B Hygriv Rao
Avinash Mudireddy - University of Iowa, Engineering Administration
Milan Sonka - University of Iowa
Roopinder K Sandhu - Libin Cardiovascular Institute of Alberta
Rose Anne Kenny - Trinity College Dublin
Giselle M Statz - University of Iowa
Rakesh Gopinathannair - Heart Rhythm Society
David Benditt - University of Minnesota
Franca Dipaola - IRCCS Humanitas Research Hospital
Mauro Gatti
Roberto Menè - University of Milano-Bicocca
Alessandro Giaj Levra
Dana Shiffer - Humanitas University
Giorgio Costantino - Ospedale Maggiore
Raffaello Furlan - IRCCS Humanitas Research Hospital
Martin H Ruwald - Gentofte Hospital
Vassilios Vassilikos - Aristotle University of Thessaloniki
Milena A Gebska - University of Iowa
Brian Olshansky - University of Iowa
Resource Type: Journal article
Publication Details: European heart journal. Digital health, Vol.6(5), pp.1046-1054
DOI: 10.1093/ehjdh/ztaf061
PMID: 40984999
PMCID: PMC12450521
NLM abbreviation: Eur Heart J Digit Health
ISSN: 2634-3916
eISSN: 2634-3916
Publisher: Oxford University Press
Language: English
Electronic publication date: 06/26/2025
Date published: 09/22/2025
Academic Unit: Electrical and Computer Engineering; Injury Prevention Research Center; Roy J. Carver Department of Biomedical Engineering; Hematology, Oncology, and Blood & Marrow Transplantation; Engineering Administration; Emergency Medicine; Cardiovascular Medicine; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; General Internal Medicine; Psychological and Quantitative Foundations; Internal Medicine; Ophthalmology and Visual Sciences
Record Identifier: 9984843600502771

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