From image to insight: an AI-enabled framework for echocardiography acquisition, reconstruction, interpretation and interaction

This narrative, perspective-style review proposes a structured framework for how artificial intelligence (AI) may reshape key steps of the echocardiography workflow. We argue that AI's main contribution is not only to automate existing tasks but to enable new approaches to data acquisition, reconstruction, interpretation, and human-system interaction. We first summarize clinically integrated and, where available, regulated AI solutions for echocardiography, including acquisition guidance, view recognition, and automated chamber/function quantification. We then outline four AI-enabled directions that are at varying stages of maturity: (i) reconstruction, in which generative models could derive more complete, four-dimensional cardiac representations from sparse ultrasound data; (ii) acquisition, where AI may serve as a real-time co-pilot to optimize information content rather than image aesthetics; (iii) interpretation, extending to 'image-free' models that learn directly from upstream radiofrequency/channel data; and (iv) interaction, using semantic or augmented-reality interfaces to reduce clinician cognitive load and improve situated decision-making. Together, these developments point to a gradual shift from subjective, image-centric reading towards more quantitative, data-driven echocardiography. Their realization will depend on prospective validation, fit-for-purpose regulatory pathways, and safeguards for fairness and safety, especially for generative and image-free paradigms. Our goal is to map these possibilities and to distinguish evidence-supported applications from those that remain conceptual.