Artificial intelligence in cardiology: an evidence map across subspecialties, 2014-26.
Authors
Affiliations (28)
Affiliations (28)
- 1st Department of Cardiology, Medical University of Warsaw, Banacha 1A, 02-097 Warsaw, Poland.
- Computational Physics Laboratory, Tampere University, Tampere, Finland.
- Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
- Cardiology Department, University Hospital Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain.
- Centro de Investigación Biomédica en Red en Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain.
- Department of Biomedical and Dental Sciences and of Morphological and Functional Images, University of Messina, Messina, Italy.
- Health Sciences University Faculty of Medicine, Department of Cardiology, Istanbul, Türkiye.
- University of Crete, School of Medicine, Heraklion, Crete, Greece.
- Cardiac Imaging Unit, Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña, Spain.
- Thorax Center, Department of Cardiology, Erasmus MC, Cardiovascular Institute, Rotterdam, The Netherlands.
- Department of Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden.
- Digital Medicine & Robotics Center, Jagiellonian University Medical College, Krakow, Poland.
- Cardiology Department, Centrum Voor Hart en Vaatziekten (CHVZ), Universitair Ziekenhuis Brussel, Brussels, Belgium.
- Department of Cardiology, H.U.B.-Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium.
- Department of Cardiology, Máxima Medical Centre, Veldhoven, The Netherlands.
- Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands.
- Cardio Pulmonary Department, IRCCS San Raffaele Roma, Rome, Italy.
- Department of Human Science and Promotion of Quality of Life, San Raffaele Open University, Rome, Italy.
- Clinical Cardiology Center, National Institute of Medicine MSWiA, Warsaw, Poland.
- Center for Postgraduate Medical Education, Warsaw, Poland.
- Department of Cardiology, Antwerp University Hospital, Antwerp, Belgium.
- Research Group Cardiovascular Diseases and Center for Research and Innovation in Care, University of Antwerp, Antwerp, Belgium.
- Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium.
- Department V Internal Medicine, University of Medicine and Pharmacy Victor Babeș Timișoara, Research Centre, Institute of Cardiovascular Diseases Timișoara, Romania.
- German Heart Center Charité, Cardiology Charité Mitte, Berlin, Germany.
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
- National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK.
- Institute of Health Informatics, University College London, London, UK.
Abstract
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. 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 (<i>n</i> = 322, 65.7%); however, advanced integration in prognostic (<i>n</i> = 122, 24.9%) and therapeutic applications remains limited (<i>n</i> = 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. The scarcity of prospective validations across different resources and geographical settings (<i>n</i> = 134, 27.3%) and randomized controlled trials (<i>n</i> = 35, 3.8%) limits the adoption by clinical guidelines and widespread implementation of AI algorithms.