了解生物医学科学中深度学习的研究前景;卡塔尔世界杯8强波胆分析科学计量分析%A Nam,Seojin %A Kim,Donghun %A Jung,Woojin %A Zhu,Yongjun %+韩国首尔西大门区延世路50号延世大学图书馆与信息科学系，03722，韩国，82 2 2123 2409,zhu@yonsei.ac.kr %K深度学习%K科学计量分析%K研究出版物%K研究前景%K研究合作%K知识扩散%D 2022 %7 22.4.2022 %9原创论文%J J Med Internet Res %G英语%X使用深度学习技术的生物医学研究的进展产生了大量相关文献。然而，缺乏科学计量学研究来提供对它们的鸟瞰。这种缺失导致了对该领域及其进展的部分和支离破碎的理解。目的:本研究旨在通过从多个角度和粒度层次分析代表研究景观的不同书目实体，获得对科学领域的定量和定性理解。方法:从PubMed数据库中检索978篇生物医学领域的深度学习研究。通过分析元数据、有影响力作品的内容和被引用的参考文献，进行科学计量分析。结果:在此过程中，我们确定了当前的领先领域、主要研究课题和技术、知识扩散和研究合作。重点是将深度学习，特别是卷积神经网络应用于放射学和医学成像，而少数研究侧重于蛋白质或基因组分析。 Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. Conclusions: This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work. %M 35451980 %R 10.2196/28114 %U //www.mybigtv.com/2022/4/e28114 %U https://doi.org/10.2196/28114 %U http://www.ncbi.nlm.nih.gov/pubmed/35451980