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

Raffaele De Lucia, Matthijs Cluitmans, Julia Ramírez, Márton Kolossváry, Andreu M Climent, José Millet, Matteo Parollo, Marco Tubaro, Maurizio Volterrani, Paul Dendale, Fabrizio D’Ascenzo, Piotr Szymański, Magnus Thorsten Jensen, Polychronis Dilaveris, Gabriel Bernardino, David Duncker, Michiel Rienstra, Rafael Vidal-Perez, Emanuela T Locati, Roderick Treskes, Mark Schuuring, Cristiana Vitale, Nay Aung, Paul Leeson, Stefan Busnatu, Lukasz Koltowski, Ruben Casado-Arroyo

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DOI: 10.1093/ehjdh/ztag154
Published: 2026-10-19
Container: European Heart Journal - Digital Health
Publisher: Oxford University Press (OUP)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/ehjdh/ztag154,
  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},
  year = {2026},
  journal = {European Heart Journal - Digital Health},
  doi = {10.1093/ehjdh/ztag154},
  url = {https://doi.org/10.1093/ehjdh/ztag154}
}

RIS

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
JO  - European Heart Journal - Digital Health
DO  - 10.1093/ehjdh/ztag154
UR  - https://doi.org/10.1093/ehjdh/ztag154
ER  -

APA

Lucia, R. D., Cluitmans, M., Ramírez, J., Kolossváry, M., Climent, A. M., Millet, J., Parollo, M., Tubaro, M., Volterrani, M., Dendale, P., D’Ascenzo, F., Szymański, P., Jensen, M. T., Dilaveris, P., Bernardino, G., Duncker, D., Rienstra, M., Vidal-Perez, R., Locati, E. T., Treskes, R., Schuuring, M., Vitale, C., Aung, N., Leeson, P., Busnatu, S., Koltowski, L., & Casado-Arroyo, R. (2026). 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. European Heart Journal - Digital Health. https://doi.org/10.1093/ehjdh/ztag154

Source records

crossref · retrieved 2026-11-09T04:05:43.770Z