Artificial intelligence in cardiology for non-experts: decoding key terms and understanding machine learning methodologies. A Scientific Statement of the ESC Working Group on e-Cardiology, the European Heart Rhythm Association (EHRA), the Association for Acute CardioVascular Care (ACVC), the Heart Failure Association (HFA), the European Association of Cardiovascular Imaging (EACVI), the European Association of Preventive Cardiology (EAPC), and the European Association of Percutaneous Cardiovascular Interventions (EAPCI) of the ESC, the ESC Regulatory Affairs Committee, and the ESC Digital Cardiology and Artificial Intelligence Committee

@article{lucia2026artificial,
  title = {{Artificial intelligence in cardiology for non-experts: decoding key terms and understanding machine learning methodologies. A Scientific Statement of the ESC Working Group on e-Cardiology, the European Heart Rhythm Association (EHRA), the Association for Acute CardioVascular Care (ACVC), the Heart Failure Association (HFA), the European Association of Cardiovascular Imaging (EACVI), the European Association of Preventive Cardiology (EAPC), and the European Association of Percutaneous Cardiovascular Interventions (EAPCI) of the ESC, the ESC Regulatory Affairs Committee, and the ESC Digital Cardiology and Artificial Intelligence Committee}},
  author = {Raffaele De Lucia and Matthijs Cluitmans and Julia Ramírez and Márton Kolossváry and Andreu M Climent and José Millet and Matteo Parollo and Marco Tubaro and Maurizio Volterrani and Paul Dendale and Fabrizio D’Ascenzo and Piotr Szymański and Magnus Thorsten Jensen and Polychronis Dilaveris and Gabriel Bernardino and David Duncker and Michiel Rienstra and Rafael Vidal-Perez and Emanuela T Locati and Roderick Treskes and Mark Schuuring and Cristiana Vitale and Nay Aung and Paul Leeson and Stefan Busnatu and Lukasz Koltowski and Ruben Casado-Arroyo},
  journal = {European Heart Journal. Digital Health},
  year = {2026},
  month = nov,
  doi = {10.1093/ehjdh/ztag154},
  url = {https://doi.org/10.1093/ehjdh/ztag154},
  note = {CC BY-NC-ND 4.0},
}

TY  - JOUR
TI  - Artificial intelligence in cardiology for non-experts: decoding key terms and understanding machine learning methodologies. A Scientific Statement of the ESC Working Group on e-Cardiology, the European Heart Rhythm Association (EHRA), the Association for Acute CardioVascular Care (ACVC), the Heart Failure Association (HFA), the European Association of Cardiovascular Imaging (EACVI), the European Association of Preventive Cardiology (EAPC), and the European Association of Percutaneous Cardiovascular Interventions (EAPCI) of the ESC, the ESC Regulatory Affairs Committee, and the ESC Digital Cardiology and Artificial Intelligence Committee
AU  - Raffaele De Lucia
AU  - Matthijs Cluitmans
AU  - Julia Ramírez
AU  - Márton Kolossváry
AU  - Andreu M Climent
AU  - José Millet
AU  - Matteo Parollo
AU  - Marco Tubaro
AU  - Maurizio Volterrani
AU  - Paul Dendale
AU  - Fabrizio D’Ascenzo
AU  - Piotr Szymański
AU  - Magnus Thorsten Jensen
AU  - Polychronis Dilaveris
AU  - Gabriel Bernardino
AU  - David Duncker
AU  - Michiel Rienstra
AU  - Rafael Vidal-Perez
AU  - Emanuela T Locati
AU  - Roderick Treskes
AU  - Mark Schuuring
AU  - Cristiana Vitale
AU  - Nay Aung
AU  - Paul Leeson
AU  - Stefan Busnatu
AU  - Lukasz Koltowski
AU  - Ruben Casado-Arroyo
PY  - 2026
DA  - 2026/11/06
JO  - European Heart Journal. Digital Health
DO  - 10.1093/ehjdh/ztag154
UR  - https://doi.org/10.1093/ehjdh/ztag154
AB  - Evidence-based medicine represents the systematic approach through which physicians today use the best available scientific evidence from clinical research to guide their clinical decision-making process. However, the emergence of artificial intelligence (AI), with its diverse models and potential applications in medicine, is leading to a new form of research driven by ‘big data’ and heterogeneous datasets, extending beyond conventional statistical analysis and thereby making AI literacy an increasingly essential competence for clinicians. Artificial intelligence applications in cardiology have shown potential to support disease diagnosis, risk prediction, workflow efficiency, and therapeutic decision-making.  However, despite these opportunities, AI may also represent a technological barrier for many physicians, due to its mathematical foundations and the speed at which models evolve. A solid understanding of AI concepts is therefore necessary for cardiologists to critically interpret results and engage with this new research landscape.  This scientific statement explores AI in cardiology from an educational perspective, outlining the key knowledge required to build adequate AI literacy. The goal is to enable cardiologists not only to avoid reliance on external experts for methodological decisions, but also to collaborate with greater insight and informed judgment.
SN  - 2634-3916
ER  -