人工智能辅助舌诊的可视化分析与临床可解释性评价OA

Objective To map the research landscape of artificial intelligence(AI)-assisted tongue diag-nosis through bibliometric analysis and to quantify its diagnostic accuracy and clinical inter-pretability through a diagnostic test accuracy(DTA)meta-analysis. Methods For the bibliometric analysis,the Web of Science Core Collection(WoSCC)was queried for English-language articles and reviews on AI-assisted tongue diagnosis published between January 1,2014 and December 31,2025,and analysed using Bibliometrix,VOSview-er,and CiteSpace,with major output dimensions including annual publication output and disciplinary distribution,journal and citation characteristics,country/region and institution-al collaboration,author networks,keyword co-occurrence,and keyword burst detection.For the DTA meta-analysis,four databases[Scopus,PubMed,Web of Science,and China Nation-al Knowledge Infrastructure(CNKI)]were searched in accordance with the Preferred Report-ing Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy(PRISMA-DTA)guidelines.A bivariate random-effects model hierarchical summary receiver operating characteristic(HSROC)was used to pool sensitivity and specificity,with subgroup analyses by disease category,AI model architecture,and sample-size strata.Methodological quality was assessed with the Quality Assessment of Diagnostic Accuracy Studies version 2(QUADAS-2)tool,and publication bias was evaluated by Deeks'funnel plot asymmetry test. Results A total of 198 publications met the bibliometric eligibility criteria.Annual output in-creased 24.5-fold(from 2 in 2014 to 49 in 2025),with the period 2022-2025 alone accounting for 65.2%of all publications.China contributed approximately 83.5%of all institutional affilia-tions,with Shanghai University of Traditional Chinese Medicine and Jiatuo Xu being the most productive institution and author,respectively.Keyword analysis identified four thematic clusters(AI and deep-learning architectures,image processing and segmentation,traditional Chinese medicine(TCM)-specific applications,and disease-specific applications)and a tem-poral evolution from traditional machine learning to deep learning and transformer-based,explainable,and multimodal AI architectures.Sixteen DTA meta-analysis studies(14 755 participants)covering metabolic and hepatic disorders,oncological and oral lesions,cardiovascular risk,diabetes,and other clinical applications were included in the DTA meta-analysis.The pooled sensitivity was 90.3%[95%confidence interval(CI):86.7%-93.1%]and the pooled specificity was 93.0%(95%CI:90.6%-94.7%);the area under the summary receiv-er operating characteristic(SROC)curve(AUC)was 0.961.Heterogeneity was substantial(I2=95.8%for sensitivity;I2=92.1%for specificity).Subgroup performance was broadly consistent across disease categories,AI architectures,and sample-size strata,and Deeks'test indicated no significant publication bias(P=0.258). Conclusion AI-assisted tongue diagnosis has progressed rapidly and shows pooled diagnos-tic performance comparable to established screening modalities,supporting its potential as a complementary and easily accessible decision-support tool.