Theme Mining and Evolutionary Analysis of Medical Artificial Intelligence Research in China Over the Past Decade from the Perspectives of SIFRank and DTM

DOI: 10.12201/bmr.202604.00081

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  Abstract: Abstract Purpose/Significance Medical artificial intelligence drives advancements in medical informatization and intelligent development. This paper reviews research literature in the field of medical AI in China over the past decade, revealing its primary research themes and developmental trends to provide data support and directional guidance for subsequent research and practice. Method/Process This study analyzed 7,059 medical AI-related publications from January 1, 2015, to September 10, 2025, in the CNKI, Wanfang, and VIP databases. Titles, keywords, and abstracts were extracted as the analysis corpus. The theme mining process began with using SIFRank to extract key phrases from the corpus, and then the Dynamic Topic Model (DTM) was applied to identify and track themes over time. Theme popularity and similarity metrics were integrated to examine research hotspots and their evolutionary characteristics across three distinct time periods. Result/Conclusion Ten core research themes in medical artificial intelligence have been identified, revealing three key evolutionary characteristics: oncology as the primary application domain, the continuous digital transformation of traditional medicine, and the trend toward systematic research development. Overall, medical AI research has now entered and will remain in a phase of deep integration for the foreseeable future. Future development in this field must not only pursue the depth and breadth of technological integration into the domestic medical system, but alsodirectly address the challenges and pain points encountered in clinical practice. By overcoming implementation barriers, it will forge a path of continuous innovation that is both pragmatic and feasible.

  Key words: Medical Artificial Intelligence; SIFRank-DTM Hybrid Model; Topic Mining; Hotspot Identification; Topic Evolution

  Submit time: 10 April 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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