Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold
Artificial intelligence (AI) is a broad term referring to any automated systems that need ‘intelligence’ to carry out specific tasks. During the last decade, AI-based techniques have been gaining popularity in a vast range of biomedical fields, including the cardiovascular setting. Indeed, the disse...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Radcliffe Medical Media
2022-12-01
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| Series: | European Cardiology Review |
| Online Access: | https://www.ecrjournal.com/articleindex/ecr.2022.11 |
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| author | Mauro Chiarito Luca Luceri Angelo Oliva Giulio Stefanini Gianluigi Condorelli |
| author_facet | Mauro Chiarito Luca Luceri Angelo Oliva Giulio Stefanini Gianluigi Condorelli |
| author_sort | Mauro Chiarito |
| collection | DOAJ |
| description | Artificial intelligence (AI) is a broad term referring to any automated systems that need ‘intelligence’ to carry out specific tasks. During the last decade, AI-based techniques have been gaining popularity in a vast range of biomedical fields, including the cardiovascular setting. Indeed, the dissemination of cardiovascular risk factors and the better prognosis of patients experiencing cardiovascular events resulted in an increase in the prevalence of cardiovascular disease (CVD), eliciting the need for precise identification of patients at increased risk for development and progression of CVD. AI-based predictive models may overcome some of the limitations that hinder the performance of classic regression models. Nonetheless, the successful application of AI in this field requires knowledge of the potential pitfalls of the AI techniques, to guarantee their safe and effective use in daily clinical practice. The aim of the present review is to summarise the pros and cons of different AI methods and their potential application in the cardiovascular field, with a focus on the development of predictive models and risk assessment tools. |
| format | Article |
| id | doaj-art-35eece2aaa9747a8be0cd34de2a75830 |
| institution | DOAJ |
| issn | 1758-3756 1758-3764 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Radcliffe Medical Media |
| record_format | Article |
| series | European Cardiology Review |
| spelling | doaj-art-35eece2aaa9747a8be0cd34de2a758302025-08-20T02:39:22ZengRadcliffe Medical MediaEuropean Cardiology Review1758-37561758-37642022-12-011710.15420/ecr.2022.11Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not GoldMauro Chiarito0Luca Luceri1Angelo Oliva2Giulio Stefanini3Gianluigi Condorelli4Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Center for Interventional Cardiovascular Research and Clinical Trials, The Zena and Michael A Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, USInstitute of Information Systems and Networking, University of Applied Sciences and Arts of Southern Switzerland, Lugano, SwitzerlandDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Cardio Center, Humanitas Research Hospital IRCCS, Rozzano, Milan, ItalyDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Cardio Center, Humanitas Research Hospital IRCCS, Rozzano, Milan, ItalyDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Cardio Center, Humanitas Research Hospital IRCCS, Rozzano, Milan, ItalyArtificial intelligence (AI) is a broad term referring to any automated systems that need ‘intelligence’ to carry out specific tasks. During the last decade, AI-based techniques have been gaining popularity in a vast range of biomedical fields, including the cardiovascular setting. Indeed, the dissemination of cardiovascular risk factors and the better prognosis of patients experiencing cardiovascular events resulted in an increase in the prevalence of cardiovascular disease (CVD), eliciting the need for precise identification of patients at increased risk for development and progression of CVD. AI-based predictive models may overcome some of the limitations that hinder the performance of classic regression models. Nonetheless, the successful application of AI in this field requires knowledge of the potential pitfalls of the AI techniques, to guarantee their safe and effective use in daily clinical practice. The aim of the present review is to summarise the pros and cons of different AI methods and their potential application in the cardiovascular field, with a focus on the development of predictive models and risk assessment tools.https://www.ecrjournal.com/articleindex/ecr.2022.11 |
| spellingShingle | Mauro Chiarito Luca Luceri Angelo Oliva Giulio Stefanini Gianluigi Condorelli Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold European Cardiology Review |
| title | Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold |
| title_full | Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold |
| title_fullStr | Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold |
| title_full_unstemmed | Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold |
| title_short | Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold |
| title_sort | artificial intelligence and cardiovascular risk prediction all that glitters is not gold |
| url | https://www.ecrjournal.com/articleindex/ecr.2022.11 |
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