(1) Background: Ultrasound (US) imaging is widely used in clinical diagnosis but is often degraded by speckle noise, which reduces image quality and can hinder interpretation. Deep learning (DL) has emerged as a promising approach for US denoising, yet its clinical applicability remains unclear. (2) Methods: A scoping review of studies published in the last four years on DL-based US denoising was conducted following PRISMA-ScR guidelines. Searches were performed in IEEE-Xplore, PubMed, ScienceDirect, Scopus, Web of Science, and Google Scholar. Data was extracted on anatomy, noise type, learning paradigm, network architecture, datasets, evaluation metrics, and performance outcomes. (3) Results: From 951 records retrieved, 36 studies were included. Most focused on breast, fetal, cardiac, and abdominal US. Convolutional neural networks (CNNs), particularly U-Net, were the most common approach, while generative adversarial network, vision transformers, and variational autoencoders were less explored. Reported peak signal-to-noise ratio ranged from 30 to 45 dB and structural similarity index measure from 0.85 to 0.97. Most studies (34 out of 36) relied on synthetic noise, 2D images and paired datasets, with limited evaluation on real clinical images. (4) Conclusion: Supervised CNN-based methods dominate US image denoising, but clinical translation is limited by reliance on synthetic data. Non-paired and no-ground-truth learning approaches remain underexplored despite their suitability for US imaging. Progress is further hindered by inconsistent evaluation protocols, limited robustness assessment on clinical tasks, and restricted dataset access. Future work should focus on standardized clinically meaningful evaluation, openly available datasets, and clinical validation to improve reliability and generalizability of DL-based US denoising methods.

(1) Background: Ultrasound (US) imaging is widely used in clinical diagnosis but is often degraded by speckle noise, which reduces image quality and can hinder interpretation. Deep learning has emerged as a promising approach for US denoising, yet its clinical applicability remains unclear. (2) Methods: A systematic review of studies published in the last three years on deep learning-based US denoising was conducted following PRISMA-DTA guidelines. Searches were performed in IEEE-Xplore, PubMed, ScienceDirect, Scopus, Web of Science, and Google Scholar. Data were extracted on Anatomy, noise type, learning paradigm, network architecture, datasets, evaluation metrics, and performance outcomes. (3) Results: from 951 records scrapped, 36 studies were included. Most focused-on breast, fetal, cardiac, and abdominal US. Convolutional neural networks (CNNs), particularly U-Net, were the most common approach, while GANs, transformers, and variational autoencoders were less explored. Reported PSNR ranged from 30-45 dB and SSIM from 0.85-0.97. Most studies (34 out of 36) relied on synthetic noise and paired datasets, with limited evaluation on real clinical images. (4) Conclusions: CNN-based methods dominate US denoising research, but translation to clinical practice is limited due to reliance on synthetic data and inconsistent evaluation metrics. Future work should focus on large benchmark datasets and standardized metrics to improve generalizability across clinical settings.