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医学教育与人工智能:基于 Web of Science 的文献计量分析(2013-2022)。

Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022).

机构信息

Second Clinical Medical College, Yunnan University of Chinese Medicine, Kunming, China.

出版信息

JMIR Med Educ. 2024 Oct 10;10:e51411. doi: 10.2196/51411.


DOI:10.2196/51411
PMID:39388721
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11486481/
Abstract

BACKGROUND: Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted. OBJECTIVE: This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013-2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education. METHODS: Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer. RESULTS: A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was "Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey." Keyword analysis revealed that "radiology," "medical physics," "ehealth," "surgery," and "specialty" were the primary focus, whereas "big data" and "management" emerged as research frontiers. CONCLUSIONS: The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.

摘要

背景:人工智能(AI)技术的渐进式进步促进了其在各个学科领域的融合。特别是,人工智能在医学教育中的应用已经成为一个重要趋势,并且取得了显著的研究成果。因此,有必要对 2013 年至 2022 年期间 AI 在医学教育中的研究现状进行全面回顾和分析。

目的:本研究旨在使用 CiteSpace 和 VOSviewer 对 2013 年至 2022 年期间的相关文献进行共被引分析,以可视化呈现 AI 在医学教育中的现有研究现状和趋势。

方法:系统检索了 Web of Science 核心数据库中 2013 年至 2022 年期间与 AI 和医学教育相关的文章。两位评审员根据标题和摘要仔细筛选最初检索到的文献,以排除与主题无关的文献。然后使用 CiteSpace 和 VOSviewer 对选定的文献进行分析和可视化处理,包括国家、机构、作者、参考文献和关键词。

结果:共检索到 2013 年至 2022 年期间与 AI 相关的 195 篇医学教育文献。研究表明,这些文献的年发表量呈逐年增加的趋势。美国是该研究领域最活跃的国家,哈佛大学医学院和多伦多大学是最活跃的机构。该领域的高产作者包括 Vincent Bissonnette、Charlotte Blacketer、Rolando F Del Maestro、Nicole Ledows、Nykan Mirchi、Alexander Winkler-Schwartz 和 Recai Yilamaz。被引频次最高的论文是“Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey”。关键词分析表明,“放射学”、“医学物理学”、“电子健康”、“外科学”和“专业”是主要研究方向,而“大数据”和“管理”则是研究前沿。

结论:本研究强调了 AI 在医学教育研究中的广阔应用前景。目前的研究方向涵盖放射学、医学信息管理等方面。随着技术的进步,这些研究方向有望进一步拓宽。迫切需要加强区域间的合作,并提高研究质量。这些发现为研究人员提供了有价值的见解,帮助他们确定研究视角并指导未来的研究方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/164680a99ed0/mededu-v10-e51411-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/29e60bbb30cd/mededu-v10-e51411-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/c1b9a751e71c/mededu-v10-e51411-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/3b12e4655483/mededu-v10-e51411-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/906aad2c373c/mededu-v10-e51411-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/2fc3cb4f5cb8/mededu-v10-e51411-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/3333e5aeba77/mededu-v10-e51411-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/6b0ce1fe5840/mededu-v10-e51411-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/164680a99ed0/mededu-v10-e51411-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/29e60bbb30cd/mededu-v10-e51411-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/c1b9a751e71c/mededu-v10-e51411-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/3b12e4655483/mededu-v10-e51411-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/906aad2c373c/mededu-v10-e51411-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/2fc3cb4f5cb8/mededu-v10-e51411-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/3333e5aeba77/mededu-v10-e51411-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/6b0ce1fe5840/mededu-v10-e51411-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6627/11486481/164680a99ed0/mededu-v10-e51411-g008.jpg

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本文引用的文献

[1]
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[2]
The effectiveness of artificial intelligence-based automated grading and training system in education of manual detection of diabetic retinopathy.

Front Public Health. 2022

[3]
Artificial Intelligence in Oral and Maxillofacial Surgery Education.

Oral Maxillofac Surg Clin North Am. 2022-11

[4]
Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum.

JMIR Med Educ. 2022-6-7

[5]
Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research.

Ther Adv Respir Dis. 2022

[6]
Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery.

Surg Endosc. 2022-9

[7]
Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions.

J Surg Educ. 2022

[8]
The Impact of Simulation Based Training on the Fundamentals of Endoscopic Surgery Performance Examination.

Ann Surg. 2023-3-1

[9]
Video Archiving and Communication System (VACS): A Progressive Approach, Design, Implementation, and Benefits for Surgical Videos.

Healthc Inform Res. 2021-4

[10]
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