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

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.

Keywords: acute coronary syndrome; artificial intelligence; cardiovascular prevention; device; heart failure; telemedicine.