ReviewInternational journal of general medicine2024
Machine Learning Research Trends in Traditional Chinese Medicine: A Bibliometric Review.
Review in International journal of general medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Development of a cold-heat syndrome classification model for children with allergic rhinitis based on multimodal data.Translational pediatrics · 2025Article
- A Review of Advances in Multimodal Treatment Strategies for Chronic Disorders of Consciousness Following Severe Traumatic Brain Injury.International journal of general medicine · 2025Review
- Heat syndrome types prediction of traditional Chinese medicine in acute ischemic stroke through deep learning: a pilot study.Frontiers in pharmacology · 2025Article
- Elucidating the multiscale mechanisms and therapeutic targets of caffeic acid in gastric cancer: a synergy of computational and experimental approaches.Frontiers in molecular biosciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Integrating Traditional Chinese Medicine (TCM) knowledge with modern technology, especially machine learning (ML), has shown immense potential in enhancing TCM diagnostics and treatment. This study aims to systematically review and analyze the trends and developments in ML applications in TCM through a bibliometric analysis. Methods: Data for this study were sourced from the Web of Science Core Collection. Data were analyzed and visualized using Microsoft Office Excel, Bibliometrix, and VOSviewer. Results: 474 documents were identified. The analysis revealed a significant increase in research output from 2000 to 2023, with China leading in both the number of publications and research impact. Key research institutions include the Shanghai University of Traditional Chinese Medicine and the China Academy of Chinese Medical Sciences. Major research hotspots identified include ML applications in TCM diagnosis, network pharmacology, and tongue diagnosis. Additionally, chemometrics with ML are highlighted for their roles in quality control and authentication of TCM products. Conclusion: This study provides a comprehensive overview of ML applications' development trends and research landscape in TCM. The integration of ML has led to significant advancements in TCM diagnostics, personalized medicine, and quality control, paving the way for the modernization and internationalization of TCM practices. Future research should focus on improving model interpretability, fostering international collaborations, and standardized reporting protocols.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.