# AI-Driven Early Detection of Cardiovascular Diseases: Reducing Healthcare Costs and improving patient Outcomes

arXiv: https://arxiv.org/abs/2506.08229 · HTML: https://mlsanity.com/paper/2506.08229/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network](https://mlsanity.com/paper/1711.08324/) (2017)

## Abstract (arXiv)

The main goal from this study is to discuss the main features of Artificial intelligence (AI) as well as their applicability for early cardiovascular Disease (CVDs) Detection, Material and Method : Systematic review approach Results : It was seen that integrating AI algorithm the diagnosis of CVDs become more accurate and lee time consuming. Conclusion: Now the concept of using AI technologies in cardiovascular health care holds the potential to transform disease management .