Artificial Intelligence in Cardiology: Why So Many Great Promises and Expectations, but Still a Limited Clinical Impact?

Nicolosi, Gian Luigi

doi:10.3390/jcm12072734

JCM

2023

DOI: 10.3390/jcm12072734

|View full text |Cite

Artificial Intelligence in Cardiology: Why So Many Great Promises and Expectations, but Still a Limited Clinical Impact?

Gian Luigi Nicolosi¹

Abstract: Looking at the extremely large amount of literature, as summarized in two recent reviews on applications of Artificial Intelligence in Cardiology, both in the adult and pediatric age groups, published in the Journal of Clinical Medicine [...]

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Personalized Cardiology

Apetrei

2024

Romanian Journal of Cardiology

View full text Add to dashboard Cite

Personalized/precision cardiology is a great hope of our times. In daily practice, we apply personalized, but not precise, cardiology: we take individual patient histories and perform individual clinical examinations, but we treat using general rule, general evidence-based guidelines… We do hope, in the near future, to take individual examination and treat using individual rules. Genuine, meaningful precision cardiology will rely heavily on genetics and artificial intelligence, but lifestyle, individual risk factors, patient and family compliance to treatment, and genetic makeup can influence diagnostic accuracy and treatment effects and should not be neglected. Extensive data exists regarding applications of artificial intelligence in the practice of precision cardiology, but we lack the systems that would translate its potential into clinical outcomes for real patients.

show abstract

Personalized Cardiology

Apetrei

2024

Romanian Journal of Cardiology

View full text Add to dashboard Cite

show abstract

Exploring the Prognostic Impact of Non-Obstructive Coronary Artery Lesions through Machine Learning

Torres-Salomón,

Rodríguez-Capitán,

Molina-Cabello

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The prognostic impact of non-obstructive coronary artery disease (CAD) remains controversial. Therefore, the objective of this study is to assess the long-term prognostic significance of non-obstructive CAD using machine learning models. We designed a multicenter retrospective, longitudinal, and observational study that included 3265 patients classified into three groups: 1426 patients with lesions < 20%, 643 patients with non-obstructive CAD (lesions 20–50%), and 1196 patients with obstructive CAD (lesions > 70%). A composite cardiovascular event (acute myocardial infarction, stroke, hospitalization due to heart failure, or cardiovascular-related death) was assessed after a mean follow-up of 43 months. To achieve this, various machine learning models were constructed. The model with the highest accuracy was selected to perform a Shapley Additive Explanations (SHAP) analysis, revealing the contribution of different variables in predicting an event. The SHAP analysis suggested that the percentage of coronary lesion was the most significant predictor of cardiovascular events. None of the models demonstrated adequate capability in predicting the event, showing only a good predictive ability for the absence of an endpoint. In conclusions, this study demonstrates how machine learning techniques may facilitate the development of high-performing models for predicting long-term cardiovascular events in patients undergoing coronary angiography.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Artificial Intelligence in Cardiology: Why So Many Great Promises and Expectations, but Still a Limited Clinical Impact?

Abstract: Looking at the extremely large amount of literature, as summarized in two recent reviews on applications of Artificial Intelligence in Cardiology, both in the adult and pediatric age groups, published in the Journal of Clinical Medicine [...]

Cited by 2 publications

References 19 publications

Personalized Cardiology

Personalized Cardiology

Exploring the Prognostic Impact of Non-Obstructive Coronary Artery Lesions through Machine Learning

Contact Info

Product

Resources

About