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August 28, 2026Journal of Cardiovascular ImagingOpen Access

AI in echocardiography reduces exam time and automates measurements while evolving toward explainable clinical reasoning.

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Why the study?

Traditional echocardiography workflows face challenges from increased clinical demand, workforce shortages, and repetitive scanning burdens, while evidence for clinical implementation of AI remains limited by single-center studies and inconsistent platform performance.

Design

Review

Key result

Artificial intelligence integration in echocardiography reduces examination time and automates measurements, evolving from single-task prediction to explainable clinical reasoning.

Authors

RHRan HeoSLSeung‐Ah LeeHCHyuk‐Jae Chang

Discussion

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Overview

AI may ease echocardiography workflow burdens amid shortages; leaves open effects on accuracy and outcomes pending prospective validation.

Key Points

  • To evaluate current evidence on artificial intelligence applications in echocardiography workflows and define requirements for responsible clinical adoption.
  • Reviewed prospective and clinical evidence evaluating AI algorithms applied to echocardiographic image acquisition, quantitative analysis, and automated interpretation.
  • Assessed diagnostic applications across structural, textural, and hemodynamic assessments including cardiomyopathy, valvular heart disease, and pericardial disorders.
  • AI integration shortens examination duration, automates quantitative measurements, and mitigates sonographer physical fatigue, shifting clinical roles toward active verification.
  • Modern model architectures expand beyond left ventricular ejection fraction to evaluate Doppler hemodynamics, myocardial texture, and complex structural abnormalities.
  • Widespread clinical implementation remains limited by single-center data dependencies, cross-platform performance inconsistencies, and potential automation bias in high-volume settings.

Structured PICO

P
Population
A narrative review evaluating the current evidence, limitations, and future goals for artificial intelligence integration across the echocardiography workflow.
I
Intervention
Artificial intelligence (AI) integration in echocardiography
C
Comparator
Conventional manual echocardiography workflow

AI integration in echocardiography is evolving from simple automated measurements to comprehensive, explainable diagnostic reasoning, promising improved workflow efficiency and expanded access.

Limitations

  • Reliance on single-center studies
  • Inconsistent performance across platforms
  • Potential for automation bias in high-volume settings
  • Dependence of AI performance on image quality
  • reliance on single-center studies
  • inconsistent performance across platforms
  • potential for automation bias in high-volume settings

Cite This Study

Heo et al. (2026) conducted a review in Cardiovascular disease. Artificial intelligence (AI) in echocardiography vs. Conventional manual workflow was evaluated. Artificial intelligence integration in echocardiography reduces examination time and automates measurements, evolving from single-task prediction to explainable clinical reasoning.

synapsesocial.com/papers/6a916ea0d15324a1df3aa7b5https://doi.org/10.1186/s44348-026-00087-4
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