Horizon scanning for innovative technologies that might be applied to medical products and requires new assessment approaches to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. The purpose of this study is to confirm that citation network analysis and text mining for bibliographic information analysis can be used for horizon scanning of the rapidly developing field of AI-based medical technologies and extract the latest research trend information from the field. We classified 119,553 publications obtained from SCI constructed with the keywords “conventional,” “machine-learning,” or “deep-learning" and grouped them into 36 clusters, which demonstrated the academic landscape of AI applications. We also confirmed that one or two close clusters included the key articles on AI-based medical image analysis, suggesting that clusters specific to the technology were appropriately formed. Significant research progress could be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster. Then we tracked recent research trends by re-analyzing “young” clusters based on the average publication year of the constituent papers of each cluster. The latest topics in AI-based medical technologies include electrocardiograms and electroencephalograms (ECG/EEG), human activity recognition, natural language processing of clinical records, and drug discovery. We could detect rapid increase in research activity of AI-based ECG/EEG a few years prior to the issuance of the draft guidance by US-FDA. Our study showed that a citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of rapidly developing AI-based medical technologies.

BackgroundThe need for a new style of clinical trials, called decentralized clinical trials (DCTs), has been increasing as they do not depend on physical visits to clinical sites. DCTs are expected to provide a new opportunity to patients who cannot participate in a clinical trial due to geographical and time limitations. For the adoption of DCTs, it is essential that medical devices with Internet of Medical Things (IoMT) and Internet of Health Things (IoHT) based technologies are developed and commercially adopted. In this study, we aimed to identify the regulatory considerations when IoMT/IoHT-based technologies are used in DCTs or products developed using DCTs.MethodTo understand the study and development field of IoMT/IoHT comprehensively and panoramically, relevant papers published in Web of Science were searched online. Subsequently, a citation network was obtained and characterized as a cluster using a text mining method to identify IoMT/IoHT-based technologies expected to be utilized in DCTs or products developed using DCTs.Result and DiscussionUpon analysis of the top 15 clusters and subsequent 51 sub-clusters, we identified the therapeutic areas (psychology, neurology) and IoMT/IoHT-based technologies (telemedicine, remote monitoring, and virtual reality) that are expected to be used in DCTs. We also identified several considerations based on the current regulatory guidance.ConclusionIoMT/IoHT-based technologies that are expected to be used or products developed using DCTs and key considerations made when they are used in DCTs were identified. The considerations could encourage conducting DCTs using IoMT/IoHT-based technologies.

ObjectivesHorizon-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 analysisResearch 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.ResultsWe 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.ConclusionWe 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 SummaryStrengths and limitations of this studyCitation 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.

Certain innovative technologies applied to medical product development require novel evaluation approaches and/or regulations. Horizon scanning for such technologies will help regulators prepare, allowing earlier access to the product for patients and an improved benefit/risk ratio. This study investigates whether citation network analysis and text mining of scientific papers could be a tool for horizon scanning in the field of immunology, which has developed over a long period, and attempts to grasp the latest research trends. As the result of the analysis, the academic landscape of the immunology field was identified by classifying 90,450 papers (obtained from PubMED) containing the keyword “immune* and t lymph*” into 38 clusters. The clustering was indicative of the research landscape of the immunology field. To confirm this, immune checkpoint inhibitors were used as a retrospective test topic of therapeutics with new mechanisms of action. Retrospective clustering around immune checkpoint inhibitors was found, supporting this approach. The analysis of the research trends over the last 3 to 5 years in this field revealed several candidate topics, including ARID1A gene mutation, CD300e, and tissue resident memory T cells, which shows notable progress and should be monitored for future possible product development. Our results have demonstrated the possibility that citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of life science fields such as immunology.

Background Establishing a horizon scanning method is critical for identifying technologies that require new guidelines or regulations. We studied the application of bibliographic citation network analysis to horizon scanning. Objective The possibility of applying the proposed method to interdisciplinary fields was investigated with the emphasis on tissue engineering and its example, three-dimensional bio-printing. Methodology and Results In all, 233,968 articles on tissue engineering, regenerative medicine, biofabrication, and additive manufacturing published between January 1, 1900 and November 3, 2021 were obtained from the Web of Science Core Collection. The citation network of the articles was analyzed for confirmation that the evolution of 3D bio-printing is reflected by tracking the key articles in the field. However, the results revealed that the major articles on the clinical application of 3D bio-printed products are located in clusters other than that of 3D bio-printers. We investigated the research trends in this field by analyzing the articles published between 2019 and 2021 and detected various basic technologies constituting tissue engineering, including microfluidics and scaffolds such as electrospinning and conductive polymers. The results suggested that the research trend of technologies required for product development and future clinical applications of the product are sometimes detected independently by bibliographic citation network analysis, particularly for interdisciplinary fields. Conclusion This method can be applied to the horizon scanning of an interdisciplinary field. However, identifying basic technologies of the targeted field and following the progress of research and the integration process of each component of technology are critical.