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21世纪以来肌肉骨骼疾病人工智能的科学计量学视角

A Scientometric Worldview of Artificial Intelligence in Musculoskeletal Diseases Since the 21st Century.

作者信息

Cao Siyang, Wei Yihao, Yue Yaohang, Wang Deli, Xiong Ao, Zeng Hui

机构信息

National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China.

Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China.

出版信息

J Multidiscip Healthc. 2024 Jul 9;17:3193-3211. doi: 10.2147/JMDH.S477219. eCollection 2024.

DOI:10.2147/JMDH.S477219
PMID:39006873
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11246091/
Abstract

PURPOSE

Over the past 24 years, significant advancements have been made in applying artificial intelligence (AI) to musculoskeletal (MSK) diseases. However, there is a lack of analytical and descriptive investigations on the trajectory, essential research directions, current research scenario, pivotal focuses, and future perspectives. This research aims to provide a thorough update on the progress in AI for MSK diseases over the last 24 years.

METHODS

Data from the Web of Science database, covering January 1, 2000, to March 1, 2024, was analyzed. Using advanced analytical tools, we conducted comprehensive scientometric and visual analyses.

RESULTS

The findings highlight the predominant influence of the USA, which accounts for 28.53% of the total publications and plays a key role in shaping research in this field. Notable productivity was seen at institutions such as the University of California, San Francisco, Harvard Medical School, and Seoul National University. Valentina Pedoia is identified as the most prolific contributor. had the highest number of publications in this area. The five most significant diseases are joint diseases, bone fractures, bone tumors, cartilage diseases, and spondylitis.

CONCLUSION

This comprehensive scientometric assessment benefits both experienced researchers and newcomers, providing quick access to essential information and fostering the development of innovative concepts in this field.

摘要

目的

在过去24年里,将人工智能(AI)应用于肌肉骨骼(MSK)疾病方面取得了重大进展。然而,对于其发展轨迹、重要研究方向、当前研究状况、关键重点以及未来前景,缺乏分析性和描述性研究。本研究旨在全面更新过去24年里人工智能在MSK疾病方面的进展情况。

方法

分析了来自科学网数据库的数据,涵盖2000年1月1日至2024年3月1日。使用先进的分析工具,我们进行了全面的科学计量和可视化分析。

结果

研究结果突出了美国的主导影响力,其占总出版物的28.53%,在塑造该领域研究方面发挥了关键作用。加利福尼亚大学旧金山分校、哈佛医学院和首尔国立大学等机构展现出显著的研究产出。瓦伦蒂娜·佩多亚被确定为贡献最多的作者,在该领域发表的文章数量最多。最主要的五种疾病是关节疾病、骨折、骨肿瘤、软骨疾病和脊柱炎。

结论

这种全面的科学计量评估对经验丰富的研究人员和新手都有益,能让他们快速获取重要信息,并促进该领域创新概念的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/8aa822d9475c/JMDH-17-3193-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/58b02074d759/JMDH-17-3193-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/275e7a6646a3/JMDH-17-3193-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/1735582c9955/JMDH-17-3193-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/b08efc528af5/JMDH-17-3193-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/1feb19f61cde/JMDH-17-3193-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/311fe9c542cc/JMDH-17-3193-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/8f704af668b9/JMDH-17-3193-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/5b7c1726b358/JMDH-17-3193-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/8aa822d9475c/JMDH-17-3193-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/58b02074d759/JMDH-17-3193-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/275e7a6646a3/JMDH-17-3193-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/1735582c9955/JMDH-17-3193-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/b08efc528af5/JMDH-17-3193-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/1feb19f61cde/JMDH-17-3193-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/311fe9c542cc/JMDH-17-3193-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/8f704af668b9/JMDH-17-3193-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/5b7c1726b358/JMDH-17-3193-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b6b/11246091/8aa822d9475c/JMDH-17-3193-g0009.jpg

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