Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review

TL;DR

This paper reviews how AI can improve echocardiography by making it more efficient and consistent, while highlighting the need for proper model development and validation.

Contribution

The paper provides a comprehensive overview of AI model development and validation practices specific to echocardiography.

Findings

AI models can assist with image acquisition, disease detection, and measurement automation in echocardiography.

Rigorous development and validation practices are essential for safe and effective AI deployment in clinical settings.

Current literature often lacks detailed descriptions of AI model validation processes in echocardiography.

Abstract

Echocardiography is a first-line, non-invasive imaging modality widely used to assess cardiac structure and function; however, its interpretation remains highly operator dependent and subject to variability. The integration of artificial intelligence (AI) into echocardiographic practice holds the potential to transform workflows, enhance efficiency, and improve the consistency of assessments across diverse clinical settings. Interest in the application of AI to echocardiography has grown significantly since the early 2000s with AI models that assist with image acquisition, disease detection, measurement automation, and prognostic stratification for various cardiac conditions. Despite this momentum, the safe and effective deployment of AI models relies on rigorous development and validation practices, yet these are infrequently described in the literature. This narrative review aims to provide a comprehensive overview of the essential steps in the development and validation of AI models for echocardiography. Additionally, it explores current challenges and outlines future directions for the integration of AI within echocardiography.

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