The Role Of Artificial Intelligence And Deep Learning In Ultrasound Diagnosis

Abstract

Ultrasound (US) remains one of the most widely used medical imaging modalities because it is real-time, radiation-free, portable, and comparatively inexpensive, but its diagnostic accuracy is strongly operator-dependent and subject to inter-observer variability. Over the past decade, artificial intelligence (AI) - and deep learning (DL) in particular - has been applied extensively to ultrasound image analysis in an effort to reduce this variability and support faster, more consistent diagnosis. This paper presents a comprehensive narrative review of current AI/DL applications in ultrasound diagnosis, synthesising evidence across four major clinical domains: breast lesion classification, thyroid nodule risk stratification, fetal cardiac screening for congenital heart disease, and abdominal/musculoskeletal applications. A structured review methodology is described, followed by a proposed conceptual framework that organises reviewed applications by deep learning task - classification, detection/localisation, and segmentation - and by clinical domain, situating an explainability layer and human-in-the-loop clinical review at the centre of responsible deployment. Reported diagnostic performance metrics (accuracy, sensitivity, specificity) from a representative set of primary studies and meta-analyses are synthesised and compared, showing that convolutional neural network (CNN)-based models frequently reach or approach expert-level diagnostic performance in constrained, retrospective evaluation settings, with reported accuracies generally in the 88-99% range and reported sensitivities in the 87-98% range across the domains reviewed. All performance figures reported in this paper are drawn directly from the cited primary studies and meta-analyses, not from a new experiment conducted by the authors. The review concludes by identifying recurring limitations across the literature - dataset heterogeneity, limited external/multicentre validation, and interpretability gaps - and outlines directions for future research toward clinically deployable, trustworthy AI-assisted ultrasound diagnosis.