European heart journal. Digital health · 2026
Artificial intelligence in cardiology: an evidence map across subspecialties, 2014-26.
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Kołtowski Łukasz, Basza Mikołaj, Soliński Mateusz, Rolek Bartosz, Brandberg Helge, Costa Francesco, Keser Nurgul, Marketou Maria, Grabowski Marcin, Vidal-Perez Rafael, Bruining Nico, Svennberg Emma, Dudek Dariusz, Cosyns Bernard, Balsam Paweł, Casado-Arroyo Ruben, Kemps Hareld, Volterrani Maurizio, Szymański Piotr, Desteghe Lien, Christodorescu Ruxandra, Opolski Grzegorz, Hindricks Gerhard, Asselbergs Folkert W
Аннотация
AIMS: Artificial intelligence (AI) is increasingly being applied in healthcare, including cardiology, with growing interest in its potential to support diagnosis, prognosis, and clinical decision-making. The rapid pace of technological advancements is not matched by equivalent progress in the regulation and appraisal of these systems. This review aims to provide a structured evidence map of AI models in cardiology, by grading the robustness of clinical validation and mapping it against the quantity of publications across main subspecialties.
METHODS AND RESULTS: To assess the strength of evidence, we defined, developed, and applied a novel classification tailored for AI models that ranks evidence into five Grades of Validation according to the level of clinical validity. We screened literature from 2014 to 2026 according to the PRISMA guidelines, identifying a total of 2807 articles on AI applications within cardiology, 490 of which met the inclusion criteria (validation was performed in at least one external, independent patient population), revealing a highly heterogeneous distribution of evidence across subspecialties. Most studies in AI focused on improved diagnostics (n = 322, 65.7%); however, advanced integration in prognostic (n = 122, 24.9%) and therapeutic applications remains limited (n = 46, 9.4%). Cardiovascular imaging leads AI applications, especially in coronary computed tomography and echocardiography, followed by non-invasive electrophysiology, predominantly in atrial fibrillation screening and ventricular dysfunction detection.
CONCLUSION: The scarcity of prospective validations across different resources and geographical settings (n = 134, 27.3%) and randomized controlled trials (n = 35, 3.8%) limits the adoption by clinical guidelines and widespread implementation of AI algorithms.
METHODS AND RESULTS: To assess the strength of evidence, we defined, developed, and applied a novel classification tailored for AI models that ranks evidence into five Grades of Validation according to the level of clinical validity. We screened literature from 2014 to 2026 according to the PRISMA guidelines, identifying a total of 2807 articles on AI applications within cardiology, 490 of which met the inclusion criteria (validation was performed in at least one external, independent patient population), revealing a highly heterogeneous distribution of evidence across subspecialties. Most studies in AI focused on improved diagnostics (n = 322, 65.7%); however, advanced integration in prognostic (n = 122, 24.9%) and therapeutic applications remains limited (n = 46, 9.4%). Cardiovascular imaging leads AI applications, especially in coronary computed tomography and echocardiography, followed by non-invasive electrophysiology, predominantly in atrial fibrillation screening and ventricular dysfunction detection.
CONCLUSION: The scarcity of prospective validations across different resources and geographical settings (n = 134, 27.3%) and randomized controlled trials (n = 35, 3.8%) limits the adoption by clinical guidelines and widespread implementation of AI algorithms.