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
{"title":"The hope and the hype of artificial intelligence for syncope management.","authors":"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","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>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.</p><p><strong>Methods and results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 5","pages":"1046-1054"},"PeriodicalIF":4.4000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12450521/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European heart journal. Digital health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/ehjdh/ztaf061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/9/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.