Journal Article Review Epublish 1 citation
PubMed: 40150752 DOI: 10.3390/bioengineering12030288
Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.
Bioengineering (Basel) 12(3)
Authors: Li Yan, Qing Li, Kang Fu, Xiaodong Zhou, Kai Zhang
Affiliations
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an 710072, China.
- Ultrasound Diagnosis & Treatment Center, Xi'an International Medical Center Hospital, Xi'an 710100, China.
- Department of Dermatology and Aesthetic Plastic Surgery, Xi'an No. 3 Hospital, The Affiliated Hospital of Northwest University, Xi'an 718000, China.
Abstract
The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI's error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI's diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes.
MeSH
Intelligence, Diagnosis, Therapeutics, Learning, Artificial Intelligence