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Artificial Intelligence (AI) has impacted every aspect of clinical medicine, and is predicted to revolutionise diagnosis, treatment and patient care. Through novel machine learning (ML) and deep learning (DL) techniques, AI has made significant grounds in cardiology and cardiac investigations, including echocardiography. Echocardiography is a ubiquitous tool that remains first-line for the evaluation of many cardiovascular diseases, with large data sets, objective parameters, widespread availability and an excellent safety profile, it represents the perfect candidate for AI advancement. As such, AI has firmly made its stamp on echocardiography, showing great promise in training, image acquisition, interpretation and analysis, diagnostics, prognostication and phenotype development. However, there remain significant barriers in real-world clinical application and uptake of AI derived algorithms in echocardiography, most importantly being the lack of clinical outcome studies. While AI has been shown to match or even best its human counterparts, an improvement in real world outcomes remains to be established. There are also legal and ethical concerns that hinder its progress. Large outcome focused trials and a collaborative multi-disciplinary effort will be necessary to push AI into the clinical workspace. Despite this, current and emerging trials suggest that these systems will undoubtedly transform echocardiography, improving clinical utility, efficiency and training. [ABSTRACT FROM AUTHOR]

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Clinical trials in the treatment of heart failure have relied on the use of a composite of hard clinical endpoints to evaluate the efficacy of the treatment arm. This has led to prolonged trials requiring large patient cohorts and extensive funding to reach statistical significance. In this paper, we have explored the potential of currently available circulating and imaging biomarkers associated with heart failure as a surrogate for hard clinical end points in clinical trials. This would be expected to result in shorter trials, smaller patient cohorts and limited funding required. We have subsequently theorized on combining circulating and imaging biomarkers as a surrogate for clinical end points such as hospitalization from heart failure and cardiac mortality., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)