TY - JOUR
T1 - The hope and the hype of artificial intelligence for syncope management
AU - Johnston, Samuel L.
AU - Barsotti, E. John
AU - Bakogiannis, Constantinos
AU - Fedorowski, Artur
AU - Ricci, Fabrizio
AU - Heller, Eric G.
AU - Sheldon, Robert S.
AU - Sutton, Richard
AU - Shen, Win Kuang
AU - Thiruganasambandamoorthy, Venkatesh
AU - Adhaduk, Mehul
AU - Parker, William H.
AU - Aburizik, Arwa
AU - Haselton, Corey R.
AU - Cuskey, Alex J.
AU - Lee, Sangil
AU - Johansson, Madeleine
AU - Macfarlane, Donald
AU - Dominic, Paari
AU - Abe, Haruhiko
AU - Rao, B. Hygriv
AU - Mudireddy, Avinash
AU - Sonka, Milan
AU - Sandhu, Roopinder K.
AU - Kenny, Rose Anne
AU - Statz, Giselle M.
AU - Gopinathannair, Rakesh
AU - Benditt, David
AU - Dipaola, Franca
AU - Gatti, Mauro
AU - Menè, Roberto
AU - Levra, Alessandro Giaj
AU - Shiffer, Dana
AU - Costantino, Giorgio
AU - Furlan, Raffaello
AU - Ruwald, Martin H.
AU - Vassilikos, Vassilios
AU - Gebska, Milena A.
AU - Olshansky, Brian
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/9/1
Y1 - 2025/9/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Clinical management
KW - Hope
KW - Hype
KW - Patient experience
KW - Syncope
UR - https://www.scopus.com/pages/publications/105016598850
U2 - 10.1093/ehjdh/ztaf061
DO - 10.1093/ehjdh/ztaf061
M3 - Review
C2 - 40984999
AN - SCOPUS:105016598850
SN - 2634-3916
VL - 6
SP - 1046
EP - 1054
JO - EUROPEAN HEART JOURNAL - DIGITAL HEALTH
JF - EUROPEAN HEART JOURNAL - DIGITAL HEALTH
IS - 5
ER -