中医智能诊断的跨学科融合与发展趋势:主题演变分析OA

Objective To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine(TCM)through quantita-tive topic evolution analysis,we addressed the fragmentation of existing research and clari-fied the long-term research structure and evolutionary patterns of the field. Methods A topic evolution analysis was performed on Chinese-language literature pertain-ing to intelligent diagnosis in TCM.Publications were retrieved from the China National Knowledge Infrastructure(CNKI),Wanfang Data,and China Science and Technology Journal Database(VIP),covering the period from database inception to July 3,2025.A hybrid seg-mentation approach,based on cumulative publication growth trends and inflection point de-tection,was applied to divide the research timeline into distinct stages.Subsequently,the la-tent Dirichlet allocation(LDA)model was used to extract research topics,followed by align-ment and evolutionary analysis of topics across different stages. Results A total of 3 919 publications published between 2003 and 2025 were included,and the research trajectory was divided into five stages based on data-driven breakpoint detec-tion.The field exhibited a clear evolutionary shift from early rule-based systems and tongue-pulse image and signal analysis(2006 – 2010),to machine-learning-based syndrome and pre-scription modeling(2011 – 2015),followed by deep-learning-driven pattern recognition and formula association(2016 – 2020).Since 2021,research has increasingly emphasized knowl-edge-graph construction,multimodal integration,and intelligent clinical decision-support systems,with recent studies(2024 – 2025)showing the emergence of large language models and agent-based diagnostic frameworks.Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis,along-side the progressive consolidation of integrated intelligent diagnostic platforms. Conclusion By identifying key technological transitions and persistent core research themes,our findings offer a structured reference framework for the design of intelligent diagnostic sys-tems,the construction of knowledge-driven clinical decision-support tools,and the align-ment of AI models with TCM diagnostic logic.Importantly,the stage-based evolutionary in-sights derived from this analysis can inform future methodological choices,improve model interpretability and clinical applicability,and support the translation of intelligent TCM diag-nosis from experimental research to real-world clinical practice.