Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis

• Published in: Open medicine (Warsaw, Poland) Vol. 19; no. 1; p. 20230874
• Main Authors: Chen, Zijian, Liu, Yangqi, Lin, Zeying, Huang, Weizhe
• Format: Journal Article
• Language: English
• Published: Poland De Gruyter 01.01.2024; Walter de Gruyter GmbH
• Subjects: bibliometric analysis; Bibliometrics; burstiness; Cancer research; collaboration; global trend; Lung cancer; Machine learning; Medical research; lung cancer; burstiness; machine learning; global trend; bibliometric analysis; collaboration
• ISSN: 2391-5463; 2391-5463
• DOI: 10.1515/med-2023-0874

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. and appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade.