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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.03698 |
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| _version_ | 1866910986580000768 |
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| author | Mo, Yuanlin Huang, Haishan Liang, Bocheng Ma, Weibo |
| author_facet | Mo, Yuanlin Huang, Haishan Liang, Bocheng Ma, Weibo |
| contents | Recent advancements in artificial intelligence (AI) have revolutionized cardiovascular medicine, particularly through integration with computed tomography (CT), magnetic resonance imaging (MRI), electrocardiography (ECG) and ultrasound (US). Deep learning architectures, including convolutional neural networks and generative adversarial networks, enable automated analysis of medical imaging and physiological signals, surpassing human capabilities in diagnostic accuracy and workflow efficiency. However, critical challenges persist, including the inability to validate input data accuracy, which may propagate diagnostic errors. This review highlights AI's transformative potential in precision diagnostics while underscoring the need for robust validation protocols to ensure clinical reliability. Future directions emphasize hybrid models integrating multimodal data and adaptive algorithms to refine personalized cardiovascular care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancements in Artificial Intelligence Applications for Cardiovascular Disease Research Mo, Yuanlin Huang, Haishan Liang, Bocheng Ma, Weibo Computer Vision and Pattern Recognition Recent advancements in artificial intelligence (AI) have revolutionized cardiovascular medicine, particularly through integration with computed tomography (CT), magnetic resonance imaging (MRI), electrocardiography (ECG) and ultrasound (US). Deep learning architectures, including convolutional neural networks and generative adversarial networks, enable automated analysis of medical imaging and physiological signals, surpassing human capabilities in diagnostic accuracy and workflow efficiency. However, critical challenges persist, including the inability to validate input data accuracy, which may propagate diagnostic errors. This review highlights AI's transformative potential in precision diagnostics while underscoring the need for robust validation protocols to ensure clinical reliability. Future directions emphasize hybrid models integrating multimodal data and adaptive algorithms to refine personalized cardiovascular care. |
| title | Advancements in Artificial Intelligence Applications for Cardiovascular Disease Research |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.03698 |