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The Hope and the Hype of Artificial Intelligence for Syncope Management

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Title: The Hope and the Hype of Artificial Intelligence for Syncope Management
Authors: Johnston, Samuel L, Barsotti, E John, Bakogiannis, Constantinos, Fedorowski, Artur, Ricci, Fabrizio, Heller, Eric G, Sheldon, Robert S, Sutton, Richard, Shen, Win-Kuang, Thiruganasambandamoorthy, Venkatesh, Adhaduk, Mehul, Parker, William H, Aburizik, Arwa, Haselton, Corey R, Cuskey, Alex J, Lee, Sangil, Johansson, Madeleine, Macfarlane, Donald, Dominic, Paari, Abe, Haruhiko, Rao, B Hygriv, Mudireddy, Avinash, Sonka, Milan, Sandhu, Roopinder K, Kenny, Rose Anne, Statz, Giselle M, Gopinathannair, Rakesh, Benditt, David, Dipaola, Franca, Gatti, Mauro, Menè, Roberto, Giaj Levra, Alessandro, Shiffer, Dana, Costantino, Giorgio, Furlan, Raffaello, Ruwald, Martin H, Vassilikos, Vassilios, Gebska, Milena A, Olshansky, Brian
Contributors: Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), EpiHealth: Epidemiology for Health, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), EpiHealth: Epidemiology for Health, Originator, Lund University, Faculty of Medicine, Department of Clinical Sciences, Malmö, Cardiovascular Research - Hypertension, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Malmö, Kardiovaskulär forskning - hypertoni, Originator
Source: European Heart Journal - Digital Health. 6(5):1046-1054
Subject Terms: Medical and Health Sciences, Clinical Medicine, Cardiology and Cardiovascular Disease, Medicin och hälsovetenskap, Klinisk medicin, Kardiologi och kardiovaskulära sjukdomar, Basic Medicine, Medical Informatics, Medicinska och farmaceutiska grundvetenskaper, Medicinsk informatik
Description: Importance 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). Observations 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 datasets, 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: (1) AI is crucial for advancing syncope management; (2) AI can enhance the patient experience; and (3) AI in syncope care is inevitable. Conclusions and Relevance AI may improve syncope diagnosis and management, particularly through machine learning (ML)-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 datasets are essential for progress. AI may enhance efficiency and communication but raises concerns regarding confidentiality, bias, inequities, and legal implications.
Access URL: https://doi.org/10.1093/ehjdh/ztaf061
Database: SwePub
Description
Abstract:Importance 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). Observations 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 datasets, 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: (1) AI is crucial for advancing syncope management; (2) AI can enhance the patient experience; and (3) AI in syncope care is inevitable. Conclusions and Relevance AI may improve syncope diagnosis and management, particularly through machine learning (ML)-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 datasets are essential for progress. AI may enhance efficiency and communication but raises concerns regarding confidentiality, bias, inequities, and legal implications.
ISSN:26343916
DOI:10.1093/ehjdh/ztaf061