Trends in Artificial Intelligence and Ultrasound Medicine: A Bibliometric and Visualized Analysis

Purpose: To analyze the development process of artificial intelligence applied to ultrasound medicine in these 6 years and to provide a reliable forecast of future trends.Methods: We conducted a literature search for Ultrasound Medicine and Artificial Intelligence research from 2017 to 2022 using the Science Citation Index Expanded (SCIE) of the Web of Science Core Collection (WoSCC). The VOS viewer, and CiteSpace software were used to perform the bibliometric analysis. Annual growth trend, countries and institutions, journals, keywords, references, and citation bursts were analyzed.Results: A total of 734 publications from 2017 to 2022 were retrieved. Annual publication records grew from 8 to more than 300 during this period. PEOPLES R CHINA had the highest number of publications (n = 251). Sun Yat-Sen University became the research institution with the largest number of publications worldwide. RADIOLOGY became the most highly cited journal. The 318 keywords were classified into 13 clusters, including hepatocellular carcinoma, breast cancer, thyroid nodule, cardiovascular disease, intravascular ultrasound, deep learning, covid-19, precision medicine, medical image processing, etc. Timezone View shows trends and relationship between research themes over time.Conclusion: The number of literatures related to artificial intelligence in ultrasound medicine has been growing rapidly in the past 6 years. For the first time, we have obtained deep insights into AI and ultrasound medicine research through bibliometric analysis. The results of this study will be useful for scholars seeking to understand basic information and identify research frontiers in the field.