The Emerging Role of Artificial Intelligence in Cardiovascular Care: From Imaging to Predictive Analytics

Abstract

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, evolving from theoretical promise to practical tools that improve diagnostic accuracy, streamline workflow, and enhance patient outcomes. Machine learning (ML) and deep learning systems now support automated image interpretation, disease detection, physiologic signal analysis, and individualized risk prediction. Large language models further reduce documentation burden and enable more efficient clinical communication. This review summarizes current applications of AI across cardiovascular care, with an example on the role of AI in predictive analytics in liver transplant candidates, an area where early risk identification may significantly alter outcomes. The article also explores emerging innovations, including the potential role of quantum computing, that may define the next era of cardiovascular medicine.

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BibTeX RIS

BibTeX

@article{hatab2026emerging,
  title = {{The Emerging Role of Artificial Intelligence in Cardiovascular Care: From Imaging to Predictive Analytics}},
  author = {Taha Hatab and Maan Fares},
  journal = {Heart Views},
  year = {2026},
  month = sep,
  doi = {10.4103/heartviews.heartviews_41_26},
  url = {https://doi.org/10.4103/heartviews.heartviews_41_26},
  note = {CC BY-NC-SA},
}

RIS

TY  - JOUR
TI  - The Emerging Role of Artificial Intelligence in Cardiovascular Care: From Imaging to Predictive Analytics
AU  - Taha Hatab
AU  - Maan Fares
PY  - 2026
DA  - 2026/09/01
JO  - Heart Views
DO  - 10.4103/heartviews.heartviews_41_26
UR  - https://doi.org/10.4103/heartviews.heartviews_41_26
AB  - Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, evolving from theoretical promise to practical tools that improve diagnostic accuracy, streamline workflow, and enhance patient outcomes. Machine learning (ML) and deep learning systems now support automated image interpretation, disease detection, physiologic signal analysis, and individualized risk prediction. Large language models further reduce documentation burden and enable more efficient clinical communication. This review summarizes current applications of AI across cardiovascular care, with an example on the role of AI in predictive analytics in liver transplant candidates, an area where early risk identification may significantly alter outcomes. The article also explores emerging innovations, including the potential role of quantum computing, that may define the next era of cardiovascular medicine.
SN  - 1995-705X
SN  - 0976-5123
ER  -

Abstract

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