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May 30, 2026Annual Review of Biomedical Data Science

Artificial Intelligence in Image-Based Cardiovascular Disease Analysis

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Why the study?

Recent advancements in artificial intelligence have significantly influenced image-based diagnostics in cardiovascular disease, warranting a structured review of its current state, applications, and future potential.

Design

Review

Key result

Artificial intelligence applications in image-based cardiovascular disease analysis offer significant potential across various imaging modalities, though challenges and limitations remain.

Authors

XWXin WangMHMingcheng HuCTConnie W. Tsao

Discussion

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Overview

Cautions against routine clinical adoption of AI in cardiovascular imaging; leaves open rigorous prospective validation studies.

Key Points

  • This review assesses the impact of artificial intelligence on image-based analysis of cardiovascular disease, focusing on current practices and future opportunities.
  • Categorized literature based on anatomical structures related to cardiovascular disease.
  • Examined various imaging modalities such as computed tomography and magnetic resonance imaging.
  • Discussed challenges and limitations of current AI-based CVD analysis methods.
  • Identified key anatomical structures affected by cardiovascular disease and their relevance to AI applications.
  • Provided insights into how different imaging techniques enhance CVD analysis using AI.
  • Outlined potential research directions to address challenges in integrating AI with cardiovascular diagnostics.

Structured PICO

I
Intervention
Artificial intelligence applications in image-based cardiovascular disease analysis

This review highlights the current state, categorization, and future potential of artificial intelligence in image-based cardiovascular disease diagnostics.

Cite This Study

Wang et al. (2026) conducted a review in Cardiovascular disease. Artificial intelligence in image-based diagnostics was evaluated. Artificial intelligence applications in image-based cardiovascular disease analysis offer significant potential across various imaging modalities, though challenges and limitations remain.

synapsesocial.com/papers/6a1a80de0307b78509432dbchttps://doi.org/10.1146/annurev-biodatasci-092624-111837
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