Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis.
Background: 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 it...
| Publicado en: | Journal of Medical Internet Research Vol. 24; no. 4 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
JMIR Publications Inc.
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156677841&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156677841 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14394456 DNC jtl: Journal of Medical Internet Research issn: 14394456 maglogo: N pubinfo: dt: Apr2022 vid: 24 iid: 4 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 156677841 156677841 NLM35451980 156677841 10.2196/28114 NLM35451980 156677841 ppct: 1 formats: tig: atl: Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis. aug: au: Nam, Seojin Kim, Donghun Jung, Woojin Zhu, Yongjun affil: 1 Department of Library and Information Science, Sungkyunkwan University, Seoul, Republic of Korea sug: subj: Research, Medical Bibliometrics Communications Media Clinical Assessment Tools Scales ab: Background: 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.Objective: 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.Methods: 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.Results: 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.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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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