Table 1_Applications of artificial intelligence in traditional Chinese medicine tongue diagnosis: a bibliometric review of research trends and hotspots, 2015–2026.docx

Publication output showed an overall upward trend in both datasets over the study period. The CNKI keyword clustering network showed high clustering quality (modularity Q = 0.7929; weighted mean silhouette S = 0.9413), as did the WoSCC network (Q = 0.8000; S = 0.9239). In CNKI, the strongest keyword bursts included “deep learning” (strength = 2.95), “image segmentation” (2.69), and “transfer learning” (2.61); in WoSCC, prominent bursts included “artificial intelligence” (2.13), “image colour analysis” (2.10), and “machine learning” (1.86).

CNKI-indexed literature showed prominent themes involving tongue-image objectification, segmentation, and TCM-oriented diagnostic concepts, whereas WoSCC-indexed literature showed more differentiated themes involving AI architectures, tongue-feature analysis, computational modelling, and disease-related applications. Highly cited publications in both datasets further reflected the importance of tongue-image extraction and segmentation and the subsequent expansion toward disease-oriented modelling.

Across both datasets, the temporal patterns suggested a shift in research attention from conventional image processing and feature extraction toward machine learning, deep learning, and more integrated AI-based approaches.</p>Conclusions<p>The two datasets showed complementary but database-specific thematic structures in AI-assisted tongue diagnosis. Although the literature indicates a shift toward more advanced and disease-oriented AI applications, these bibliometric patterns do not establish the scientific or medical validity, diagnostic accuracy, or clinical readiness of AI-assisted tongue analysis.

Further standardization, multicentre external validation, and clinically interpretable and reproducible research are needed before broader clinical application.</p>