Artificial Intelligence And Deep Learning In Ultrasound Diagnosis: A Comprehensive Review Of Clinical Applications, Diagnostic Accuracy, And Future Perspectives

Abstract

Artificial intelligence (AI) and deep learning (DL) technologies have revolutionized medical imaging and diagnostics. This comprehensive review synthesizes current evidence on the application, diagnostic accuracy, challenges, and future directions of AI algorithms in ultrasound (US) imaging. We conducted a systematic analysis of peer-reviewed literature examining diagnostic accuracy of deep learning in medical imaging and specific AI applications in ultrasound-guided procedures, breast US diagnosis, AI-based radiomics, and regional anesthesia guidance. Our findings demonstrate that AI algorithms achieve high diagnostic accuracy across multiple ultrasound applications, with sensitivity and specificity often comparable to or exceeding experienced radiologists. In ophthalmology imaging, AI achieved area under curve (AUC) of 0.939-0.969 for various retinal pathologies. In breast ultrasound, AI systems demonstrated sensitivity of 92.5% and accuracy of 78.6% for malignant lesion detection. Deep learning radiomics achieved AUC of 0.978 in pancreatic adenocarcinoma diagnosis and 0.97 in breast cancer characterization. However, significant challenges remain including standardization of training datasets, external validation, clinical workflow integration, regulatory compliance, and reimbursement issues. This review highlights that while AI shows tremendous promise for enhancing diagnostic accuracy and clinical efficiency in ultrasound medicine, successful implementation requires multidisciplinary collaboration, robust validation frameworks, and organizational infrastructure for sustainable clinical integration.