Objective: To map the research landscape of artificial intelligence (AI)-assisted tongue diagnosis through bibliometric analysis and to quantify its diagnostic accuracy and clinical interpretability 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, VOSviewer, and CiteSpace, with major output dimensions including annual publication output and disciplinary distribution, journal and citation characteristics, country/region and institutional collaboration, author networks, keyword co-occurrence, and keyword burst detection. For the DTA meta-analysis, four databases [Scopus, PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI)] were searched in accordance with the Preferred Reporting 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 increased 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 affiliations, 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 temporal 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 receiver 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 diagnostic performance comparable to established screening modalities, supporting its potential as a complementary and easily accessible decision-support tool.

Large language models (LLMs) offer a modern approach to help inherit traditional Chinese medicine (TCM). This article discussed the progress of LLM applications in TCM and proposed future development directions by reviewing the existing research. We have found that LLMs and related technologies have excellent applications and performance in the management of TCM knowledge and data. They are often applied in information extraction, knowledge graph construction, and data standardization processing. However, data quality and security issues need to be given more attention. In clinical diagnosis and treatment, LLMs can imitate the thinking of TCM by disassembling and reconstructing its diagnostic process and can achieve functions such as prescription recommendation and question and answer (Q&A). However, this approach involves LLMs making inferences and predictions based on existing corpora and thus may not flexibly handle complex environments and tasks. Moreover, the current evaluation criteria for TCM LLMs can be summarized into 3 categories: general evaluation metrics, technical framework evaluation, and evaluation criteria for the characteristics of TCM (such as consistency rates of prescriptions and diagnostic suggestions). However, the lack of a unified and standardized evaluation system hinders the clinical application of TCM LLMs. The future progress of TCM LLMs should focus on the 3 aforementioned critical aspects to achieve technological breakthroughs. In addition, we are promoting the research on vertical TCM LLMs and application terminals. We believe this will bring new ideas to the research on TCM LLMs.

Elderly patients with tumors face unique challenges due to age-related decline in organ function, multiple comorbidities, and poor tolerance to treatment, often limiting the applicability of the traditional “maximum tolerated therapy” approach in this population. This article aims to explore the “green therapy” system tailored for tumors in elderly patients. Proposed by Professor Hu Kaiwen in 2003, this system replaces “cure” with “control,” integrating modern minimally invasive techniques with holistic regulation from Traditional Chinese Medicine to form a treatment strategy that balances local and systemic approaches. This paper systematically elucidates the construction and application of this system in tumors in elderly patients, highlighting its core value of achieving long-term peaceful coexistence between patients and tumors through low-damage methods, thereby offering a new treatment option aligned with the physiological characteristics of this population.

Objective To comprehensively evaluate the quality difference of Huaihua powder obtained by the hospital's electromagnetic furnace decoction method and automatic decoction machine decoction method.Methods Five methods(induction cooker decoction(once decoction,twice decoction),normal pressure decoction(once decoction,twice decoction),and pressure decoction)were used to prepare the decoction.The HPLC method used a Shiseido CAPCELL PAK-C18(250 mm×4.6 mm,5 μm)column,with a UV detection wavelength of 254 nm,mobile phase of acetonitrile 0.1%phosphoric acid water,column temperature of 30℃,flow rate of 1.0 mL/min,and detection of index components including rutin,naringin,quercitrin,neohesperidin,quercetin,and menthone.The extraction rate of the index components was calculated and the cream yield of each decoction was detected.The evaluation of quality differences is based on the comprehensive score of cream yield(40%)and indicator components(60%).Results The HPLC fingerprint similarity of Huaihua powder prepared by 5 decoction methods was greater than 99%.There was no significant difference(P>0.05)in the yield of ointment and the content of indicator components between the induction cooker one decoction and the induction cooker twice decoction methods.The yield of ointment and the extraction rate of indicator components were significantly higher in twice normal pressure decoction using a decoction machine than once normal pressure decoction using a decoction machine(P<0.05).The index component score of machine under pressure decoction once was the highest,but the comprehensive score of twice normal pressure decoction was the highest,with a combined score of 92.13 and 89.58,respectively,far higher than the 36.01,18.79,and 15.60 of the other three decoction methods.Classify the decoction into three categories through cluster analysis.Orthogonal partial least squares-discriminant analysis was used to screen for rutin,naringin and neohesperidin as differential biomarkers.Conclusion The quality of Huaihua powder decocted by a decoction machine is significantly better than that by an induction cooker,and the twice decocted at normal pressure by the decoction machine are significantly better than those decocted at normal pressure once.Decoction under pressure once may be a better choice.

Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

With the aging of the population,the proportion of older patients with cancer is increasing.The treatment of older patients with cancer is challenging owing to frailty,comorbidities,and the exclusion from most clinical studies.Green therapy,using low-injury,sustainable,repeatable,and individualized treatment methods—such as minimally invasive surgery and traditional Chinese medicine—provides a new option for patients with cancer who are intolerant or have failed in conventional treatment.This article summarizes the safety and effectiveness of minimally invasive surgery,such as radiofrequency ablation,microwave ablation,cryoablation,and vascular interventional therapy,in older patients with tumors,as well as external treatment of traditional Chinese medicine in clinical practice to provide a basis for green treatment of tumors.Prospective clinical trials should be performed on older patients with cancer to provide high-level evidence-based medical evidence for the treatment of these patients.Furthermore,a clinical prediction model of green treatment based on real-world data should be constructed for older patients with cancer to provide a foundation for clinical medical decision-making.Basic researches should be conducted to explore the mechanism of green tumor treatment.

With the aging of the population,the proportion of older patients with cancer is increasing.The treatment of older patients with cancer is challenging owing to frailty,comorbidities,and the exclusion from most clinical studies.Green therapy,using low-injury,sustainable,repeatable,and individualized treatment methods—such as minimally invasive surgery and traditional Chinese medicine—provides a new option for patients with cancer who are intolerant or have failed in conventional treatment.This article summarizes the safety and effectiveness of minimally invasive surgery,such as radiofrequency ablation,microwave ablation,cryoablation,and vascular interventional therapy,in older patients with tumors,as well as external treatment of traditional Chinese medicine in clinical practice to provide a basis for green treatment of tumors.Prospective clinical trials should be performed on older patients with cancer to provide high-level evidence-based medical evidence for the treatment of these patients.Furthermore,a clinical prediction model of green treatment based on real-world data should be constructed for older patients with cancer to provide a foundation for clinical medical decision-making.Basic researches should be conducted to explore the mechanism of green tumor treatment.

Objective To comprehensively evaluate the quality difference of Huaihua powder obtained by the hospital's electromagnetic furnace decoction method and automatic decoction machine decoction method.Methods Five methods(induction cooker decoction(once decoction,twice decoction),normal pressure decoction(once decoction,twice decoction),and pressure decoction)were used to prepare the decoction.The HPLC method used a Shiseido CAPCELL PAK-C18(250 mm×4.6 mm,5 μm)column,with a UV detection wavelength of 254 nm,mobile phase of acetonitrile 0.1%phosphoric acid water,column temperature of 30℃,flow rate of 1.0 mL/min,and detection of index components including rutin,naringin,quercitrin,neohesperidin,quercetin,and menthone.The extraction rate of the index components was calculated and the cream yield of each decoction was detected.The evaluation of quality differences is based on the comprehensive score of cream yield(40%)and indicator components(60%).Results The HPLC fingerprint similarity of Huaihua powder prepared by 5 decoction methods was greater than 99%.There was no significant difference(P>0.05)in the yield of ointment and the content of indicator components between the induction cooker one decoction and the induction cooker twice decoction methods.The yield of ointment and the extraction rate of indicator components were significantly higher in twice normal pressure decoction using a decoction machine than once normal pressure decoction using a decoction machine(P<0.05).The index component score of machine under pressure decoction once was the highest,but the comprehensive score of twice normal pressure decoction was the highest,with a combined score of 92.13 and 89.58,respectively,far higher than the 36.01,18.79,and 15.60 of the other three decoction methods.Classify the decoction into three categories through cluster analysis.Orthogonal partial least squares-discriminant analysis was used to screen for rutin,naringin and neohesperidin as differential biomarkers.Conclusion The quality of Huaihua powder decocted by a decoction machine is significantly better than that by an induction cooker,and the twice decocted at normal pressure by the decoction machine are significantly better than those decocted at normal pressure once.Decoction under pressure once may be a better choice.

Non-small cell lung cancer is one of the cancers with the highest incidence and mortality rate in the world, and precise prognostic models can guide clinical treatment plans. With the continuous upgrading of computer technology, deep learning as a breakthrough technology of artificial intelligence has shown good performance and great potential in the application of non-small cell lung cancer prognosis model. The research on the application of deep learning in survival and recurrence prediction, efficacy prediction, distant metastasis prediction, and complication prediction of non-small cell lung cancer has made some progress, and it shows a trend of multi-omics and multi-modal joint, but there are still shortcomings, which should be further explored in the future to strengthen model verification and solve practical problems in clinical practice.

Objective Network Meta-analysis was conducted to evaluate the differences in the efficacy of different traditional Chinese medicine injections(TCMI)in patients with cancer-related fatigue.Methods The database was systematically searched and included in Cochrane Library,PubMed,Web of Science,CNKI,Wan Fang,China Biomedical Literature Database and Database of Chinese sci-tech periodicals until September,2022.These relevant randomized controlled trial literatures were evaluated the methodological quality by using of Cochrane risk of bias assessment tool.Results 18 articles involving 1404 patients and 6 traditional Chinese medicine injections(TCMI)were included.The results showed that Kangai injection[MD=-2.30,95％CI(-3.09,-1.52)]and Aidi injection[MD=-1.31,95％CI(-1.99,-0.62)]combined with chemotherapy were better than chemotherapy alone in improving CRF in cancer patients during chemotherapy,among which Kangai injection is more effective[MD=-1.05,95％CI(-1.89,-0.21)];Shenmai injection[MD=-1.24,95％CI(-1.91,-0.57)],Kangai injection[MD=-1.02,95％CI(-1.65,-0.39)],and Shenqifuzheng injection[MD=-0.54,95％CI(-0.90,-0.19)]were superior to palliative care alone in improving CRF in palliative care of cancer patients,among which Shenmai injection[MD=-1.26,95％CI(-2.15,-0.37)]is more effective.Conclusions The results demonstrated that TCMI can improve the degree of cancer-related fatigue in tumor patients at different stages.When combined with chemotherapy,Kangai injection was superior to other TCMI,while when combined with palliative treatment,Shenmai injection was superior to other TCMI.