Expert consensus document on artificial intelligence of the Italian Society of Cardiology

Artificial intelligence (AI), a branch of computer science focused on developing algorithms that replicate intelligent behaviour, has recently been used in patients management by enhancing diagnostic and prognostic capabilities of various resources such as hospital datasets, electrocardiograms and echocardiographic acquisitions. Machine learning (ML) and deep learning (DL) models, both key subsets of AI, have demonstrated robust applications across several cardiovascular diseases, from the most diffuse like hypertension and ischemic heart disease to the rare infiltrative cardiomyopathies, as well as to estimation of LDL cholesterol which can be achieved with better accuracy through AI. Additional emerging applications are encountered when unsupervised ML methodology shows promising results in identifying distinct clusters or phenotypes of patients with atrial fibrillation that may have different risks of stroke and response to therapy. Interestingly, since ML techniques do not analyse the possibility that a specific pathology can occur but rather the trajectory of each subject and the chain of events that lead to the occurrence of various cardiovascular pathologies, it has been considered that DL, by resembling the complexity of human brain and using artificial neural networks, might support clinical management through the processing of large amounts of complex information; however, external validity of algorithms cannot be taken for granted, while interpretability of the results may be an issue, also known as a "black box" problem. Notwithstanding these considerations, facilities and governments are willing to unlock the potential of AI in order to reach the final step of healthcare advancements while ensuring that patient safety and equity are preserved.

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Publication Details

Journal
Journal of Cardiovascular Medicine
Published
2025-03-28
DOI
https://doi.org/10.2459/jcm.0000000000001716
Citations
4
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
2.62
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article

Expert consensus document on artificial intelligence of the Italian Society of Cardiology

Paolo Calabrò, Michele Ciccarelli, Nicola Leone, Gianfranco Sinagra et al.
4 citations
Journal of Cardiovascular Medicine
Artificial Intelligence in Healthcare and Education
2.62
article

Expert consensus document on artificial intelligence of the Italian Society of Cardiology

Paolo Calabrò, Michele Ciccarelli, Nicola Leone, Gianfranco Sinagra, Marco Matteo Ciccone, Antonio Curcio, Saverio Muscoli, Grzegorz Bilo, Stefania Paolillo, Gaetano Maria De Ferrari, Aldostefano Porcari, Andrea Mariani, Savina Nodari, Andrea Barison, Alberto Polimeni, Michela Casella, Stefano Carugo, Francesco Barillà, Ciro Indolfi, Carmen Spaccarotella, Marco Merlo, Italo Porto, Giovanni Esposito, Gianluigi Greco, Stefano Benenati, Pasquale Perrone Filardi, Carmine Dario Vizza, Emanuela H. Locati, Natale Daniele Brunetti, Giuseppe Boriani, Iacopo Olivotto
article en
4 citations

Abstract

Artificial intelligence (AI), a branch of computer science focused on developing algorithms that replicate intelligent behaviour, has recently been used in patients management by enhancing diagnostic and prognostic capabilities of various resources such as hospital datasets, electrocardiograms and echocardiographic acquisitions. Machine learning (ML) and deep learning (DL) models, both key subsets of AI, have demonstrated robust applications across several cardiovascular diseases, from the most diffuse like hypertension and ischemic heart disease to the rare infiltrative cardiomyopathies, as well as to estimation of LDL cholesterol which can be achieved with better accuracy through AI. Additional emerging applications are encountered when unsupervised ML methodology shows promising results in identifying distinct clusters or phenotypes of patients with atrial fibrillation that may have different risks of stroke and response to therapy. Interestingly, since ML techniques do not analyse the possibility that a specific pathology can occur but rather the trajectory of each subject and the chain of events that lead to the occurrence of various cardiovascular pathologies, it has been considered that DL, by resembling the complexity of human brain and using artificial neural networks, might support clinical management through the processing of large amounts of complex information; however, external validity of algorithms cannot be taken for granted, while interpretability of the results may be an issue, also known as a "black box" problem. Notwithstanding these considerations, facilities and governments are willing to unlock the potential of AI in order to reach the final step of healthcare advancements while ensuring that patient safety and equity are preserved.

Journal of Cardiovascular MedicineVol. 26(5)
University of Foggia (IT), University of Rome Tor Vergata (IT), University of Modena and Reggio Emilia (IT), University of Salerno (IT), University of Trieste (IT), University of Milan (IT), University of Campania "Luigi Vanvitelli" (IT), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT), Brescia University (US), Ospedale Maggiore (IT), IRCCS Policlinico San Donato (IT), Federico II University Hospital (IT), Ospedale Annunziata di Cosenza (IT), IRCCS Istituto Auxologico Italiano (IT), Policlinico Tor Vergata (IT), Ospedale Policlinico San Martino (IT), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Fondazione Toscana Gabriele Monasterio (IT), Azienda Sanitaria Universitaria Integrata di Trieste (IT), University of Florence (IT), University of Calabria (IT), University of Turin (IT), University of Brescia (IT), University of Genoa (IT), Sapienza University of Rome (IT)
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Openalex Percentile: Top 9%
Artificial Intelligence in Healthcare and Education
2.62
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