This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

Objective: To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine (TCM) through quantitative topic evolution analysis, we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field. Methods: A topic evolution analysis was performed on Chinese-language literature pertaining 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 segmentation approach, based on cumulative publication growth trends and inflection point detection, was applied to divide the research timeline into distinct stages. Subsequently, the latent Dirichlet allocation (LDA) model was used to extract research topics, followed by alignment 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 detection. 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 prescription modeling (2011 – 2015), followed by deep-learning-driven pattern recognition and formula association (2016 – 2020). Since 2021, research has increasingly emphasized knowledge-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, alongside 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 systems, the construction of knowledge-driven clinical decision-support tools, and the alignment of AI models with TCM diagnostic logic. Importantly, the stage-based evolutionary insights derived from this analysis can inform future methodological choices, improve model interpretability and clinical applicability, and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.

Background:Faecalibacterium prausnitzii(Fp)is a commensal intestinal bacterium that exhibits anti-inflammatory and immunomodulatory capacity in vivo and in vitro. It has been reported that Fp in intestinal lumen was reduced in patients with colorectal cancer,which might be a factor associated with cancer development. Aims:To investigate the effect and immunological mechanism of Fp and its genomic DNA(fDNA)on the killing activity of peripheral blood mononuclear cells (PBMCs)against human colon cancer LoVo cells. Methods:PBMCs derived from healthy adults were co-cultured in vitro with Fp,fDNA,or the digested fDNA(d-fDNA),respectively. Killing activity of PBMCs against LoVo cells was measured by MTT assay;concentrations of interferon-gamma(INF-γ),a Th1-type cytokine and interleukin-4(IL-4),a Th2-type cytokine in culture supernatant of PBMCs were determined by ELISA;and expressions of T-bet and GATA3,the transcription factors specific for Th1 and Th2 cells,were measured by real-time PCR. Results:Compared with the PBMCs not treated,fDNA could significantly enhance the killing activity of PBMCs against LoVo cells(P < 0. 05);meanwhile,it promoted IFN-γ secretion,up-regulated T-bet mRNA expression and inhibited IL-4 secretion and GATA3 mRNA expression in PBMCs(P < 0. 05). Similar effects were not observed in PBMCs treated with Fp and d-fDNA. Conclusions:fDNA enhances the killing activity of PBMCs against human colon cancer cells by up-regulating Th1 immune response.

Background:Faecalibacterium prausnitzii( Fp) is one of the most abundant bacterium in human intestinal microbiota,and is closely correlated with the process of colitis-associated colorectal cancer(CAC). Aims:To observe the effect of Fp on CAC,and investigate the possible mechanism. Methods:The model of CAC was induced by azoxymethane (AOM)and dextran sodium sulfate( DSS). Fifty-two C57BL/ 6J mice were randomly divided into 4 groups:group A (AOM + DSS),group B(AOM + DSS + Fp),group C(AOM + DSS + Fp supernatant)and group D(control group). All the mice were sacrificed on day 92. DAI was assessed,serum levels of TNF-α and IL-10 were determined by ELISA. HE staining was used to examine the grade of tumor. Expressions of VEGF,COX-2,NF-κB in tumor tissue were measured by immunohistochemistry. Results:The tumorigenesis rates of group A,B,C were 100% ,100% and 77. 8% ,respectively;mainly were high-grade intraepithelial neoplasia. The tumor load in group A was significantly higher than that in group B (P < 0. 01),and the spleen index in group B was significantly higher than that in group C(P < 0. 01). Serum level of TNF-α was significantly lower(P < 0. 05)and IL-10 was significantly higher(P < 0. 05)in group A than that in group B. No significant differences in expressions of VEGF,COX-2,NF-κB were found among group A,B and C. Conclusions:Fp had no obvious effect on the occurrence rate of CAC,and Fp supernatant could decrease the incidence of CAC in mice. Fp and its supernatant could reduce the tumor load via regulating the expressions of TNF-α,IL-10.