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PreprintCited by 11scientometrics and bibliometrics researchArtificial Intelligence in Healthcare and EducationDiverse Approaches in Healthcare and Education Studies

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Study on horizon-scanning with a focus on the development of AI-based medical products: citation network analysis

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Takata, Takuya, Sasaki, Hajime, Yamano, Hiroko, Honma, Masashi, Shikano, Mayumi·2021·Therapeutic Innovation & Regulatory Science
DOI 10.1101/2021.02.25.21252496PMC PMC8854249

Preprint — published as 10.1007/s43441-021-00355-z

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Abstract

Objectives: Horizon-scanning for innovative technologies that might be applied to medical products and require new assessment approaches/regulations will help to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. In this study, we focused on the field of AI-based medical image analysis as a retrospective example of medical devices, where many products have recently been developed and applied. We proposed and validated horizon-scanning using citation network analysis and text mining for bibliographic information analysis. Methods and analysis: Research papers for citation network analysis which contain “convolutional*” OR “machine-learning” OR “deep-learning” were obtained from Science Citation Index Expanded (SCI-expanded) in the Web of Science (WoS). The citation network among those papers was converted into an unweighted network with papers as nodes and citation relationships as links. The network was then divided into clusters using the topological clustering method and the characteristics of each cluster were confirmed by extracting a summary of frequently cited academic papers, and the characteristic keywords, in the cluster. Results: We classified 119,553 publications obtained from SCI and grouped them into 36 clusters. Hence, it was possible to understand the academic landscape of AI applications. The key articles on AI-based medical image analysis were included in one or two clusters, suggesting that clusters specific to the technology were appropriately formed. Based on the average publication year of the constituent papers of each cluster, we tracked recent research trends. It was also suggested that significant research progress would be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster. Conclusion: We validated that citation network analysis applies to the horizon-scanning of innovative medical devices and demonstrated that AI-based electrocardiograms and electroencephalograms can lead to the development of innovative products. Article Summary: Strengths and limitations of this study: Citation network analysis can provide an academic landscape in the investigated research field, based on the citation relationship of research papers and objective information, such as characteristic keywords and publication year. It might be possible to detect possible significant research progress and the emergence of new research areas through analysis every several months. It is important to confirm the opinions of experts in this area when evaluating the results of the analysis. Information on patents and clinical trials for this analysis is currently unavailable.

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Fig. 1 Steps of clustering and making Academic Landscape based on citation network. This figure has been published in reference [10]. The procedure of the citation network is as follows: (1) Extract the dataset of academic papers for analysis. (2) To extract the data, convert the citation network into an unweighted network with papers as nodes and citation relationships as links. (3) Divide the network into several clusters by using the topological clustering method. (4) Use a large graph layout (LGL), based on a force-direct layout algorithm, to display the largest connected component of the network to generate coordinates for the nodes in two dimensions and to visualize the citation network by expressing inter-cluster links with the same color.
Fig. 1 Steps of clustering and making Academic Landscape based on citation network. This figure has been published in reference [10]. The procedure of the citation network is as follows: (1) Extract the dataset of academic papers for analysis. (2) To extract the data, convert the citation network into an unweighted network with papers as nodes and citation relationships as links. (3) Divide the network into several clusters by using the topological clustering method. (4) Use a large graph layout (LGL), based on a force-direct layout algorithm, to display the largest connected component of the network to generate coordinates for the nodes in two dimensions and to visualize the citation network by expressing inter-cluster links with the same color.CC BY · PubMed Central
Fig. 2 Tracking clusters containing key articles. We analyzed papers obtained from WoS published up to the indicated years. We plotted the cluster numbers that contained the eight key articles shown in Table 1, with the circle sizes representing the approximate number of citations in the cluster for each paper
Fig. 2 Tracking clusters containing key articles. We analyzed papers obtained from WoS published up to the indicated years. We plotted the cluster numbers that contained the eight key articles shown in Table 1, with the circle sizes representing the approximate number of citations in the cluster for each paperCC BY · PubMed Central
Fig. 3 Tracking clusters related to ECG and EEG. We analyzed papers obtained from WoS published up to the indicated years. A cluster number indicates the cluster on ECG and EEG. The circle sizes indicate the approximate citation frequency of the key article, [73] and the number in each circle represents the number of citations in the cluster. Clusters on ECG and EGG were first detected in 2015 as cluster number 10 and were classified into cluster numbers 11, 21, 1, 15, and 15 for 2016, 2017, 2018, 2019, and 2020, respectively
Fig. 3 Tracking clusters related to ECG and EEG. We analyzed papers obtained from WoS published up to the indicated years. A cluster number indicates the cluster on ECG and EEG. The circle sizes indicate the approximate citation frequency of the key article, [73] and the number in each circle represents the number of citations in the cluster. Clusters on ECG and EGG were first detected in 2015 as cluster number 10 and were classified into cluster numbers 11, 21, 1, 15, and 15 for 2016, 2017, 2018, 2019, and 2020, respectivelyCC BY · PubMed Central

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Metadata

Authors
Takata, T., Sasaki, H., Yamano, H., Honma, M., & Shikano, M.
Affiliations
  • Tokyo University of Science (JP)
  • Institute for Future Initiatives, The University of Tokyo (JP)
  • University of Tokyo Hospital (JP)
Corresponding author
Shikano, Mayumi
ORCID
Venue
Therapeutic Innovation & Regulatory Science
Publisher
openRxiv
Published
2021-03-01
Volume
56
Issue
2
Pages
263-275
ISSN
2168-4790 (ISSN-L)
DOI
10.1101/2021.02.25.21252496
PMC
PMC8854249
OpenAlex
W4220993218
Cited by
11
References
1
Citations by year
2025: 3 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 2
FWCI
1.16
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Therapeutic Innovation & Regulatory Science (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Japan Agency for Medical Research and Development (JP20mk0101155)
Topics
  • Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Diverse Approaches in Healthcare and Education Studies
Keywords
horizon scanning, citation network analysis, text mining, research trends, machine learning, deep learning, medical image analysis, human activity recognition, clinical natural language processing, drug discovery, FDA draft guidance, regulatory science
MeSH terms
Artificial Intelligence, Humans, Technology, Data Mining

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CoChat. (2026). Citation check: Study on horizon-scanning with a focus on the development of AI-based medical products: citation network analysis. https://app.cochat.ai/papers/10.1101/2021.02.25.21252496
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Takata, T., Sasaki, H., Yamano, H., Honma, M., & Shikano, M. (2021). Study on horizon-scanning with a focus on the development of AI-based medical products: citation network analysis. Therapeutic Innovation & Regulatory Science. https://doi.org/10.1101/2021.02.25.21252496