# Advancements in Artificial Intelligence Applications for Cardiovascular Disease Research

arXiv: https://arxiv.org/abs/2506.03698 · HTML: https://mlsanity.com/paper/2506.03698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once](https://mlsanity.com/paper/2405.12971/) (2024)
- [CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos](https://mlsanity.com/paper/2410.20769/) (2024)
- [Vision Foundation Models in Medical Image Analysis: Advances and Challenges](https://mlsanity.com/paper/2502.14584/) (2025)
- [Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels](https://mlsanity.com/paper/2308.07717/) (2023)

## Abstract (arXiv)

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.