Department of Library and Information Science, Sungkyunkwan University, Seoul, Republic of Korea.
Department of Library and Information Science, Yonsei University, Seoul, Republic of Korea.
J Med Internet Res. 2022 Apr 22;24(4):e28114. doi: 10.2196/28114.
Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress.
This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity.
We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references.
In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. 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.
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.
深度学习技术在生物医学研究中的进步产生了大量相关文献。然而，缺乏科学计量学研究来提供全面的视角。这导致了对该领域及其进展的片面和碎片化理解。
本研究旨在通过分析来自多个角度和粒度的不同文献实体，从定量和定性两个方面来理解科学领域。
我们从 PubMed 数据库中搜索并检索了 978 篇生物医学深度学习研究。通过分析元数据、有影响力作品的内容和引用参考文献，进行了科学计量分析。
在此过程中，我们确定了当前的主导领域、主要研究主题和技术、知识扩散以及研究合作。主要集中在将深度学习，特别是卷积神经网络，应用于放射学和医学影像学，而少数研究则侧重于蛋白质或基因组分析。放射学和医学影像学似乎也是知识扩散的最重要的知识来源和重要领域，其次是计算机科学和电气工程。合著分析揭示了工程学和生物医学学科群之间的各种合作。
本研究调查了生物医学深度学习研究的现状，并证实了其跨学科性质。尽管它已经取得了成功，但我们认为在某些领域需要更多样化的应用，以进一步推动深度学习在解决生物医学研究问题方面的贡献。我们期望本研究的结果能帮助研究人员和社区更好地调整他们当前和未来的工作。
