ArticleHeliyon2024
Uncovering the scientific landscape: A bibliometric and Visualized Analysis of artificial intelligence in Traditional Chinese Medicine.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- Research Trends and Dynamics in Single-cell RNA Sequencing for Musculoskeletal Diseases: A Scientometric and Visualization Study.International journal of medical sciences · 2025Review
- Deciphering Epigenetic and Post-Translational Modifications in Ferroptosis: A Scientometric and Visualization Study.International journal of medical sciences · 2025Article
- Unveiling the dynamic trends of plant-derived exosome nanovesicles-based theranostics: through bibliometric and visualized analysis.Frontiers in medicine · 2025Article
- Article
- Traditional Chinese medicine treatment strategies for primary dysmenorrhea.Frontiers in endocrinology · 2025Review
- A scientometric and visualization analysis of 3D printing scaffolds for vascularized bone tissue engineering over the last decade.Regenerative therapy · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of artificial intelligence (AI) technology has presented new challenges and opportunities for Traditional Chinese Medicine (TCM), aiming to provide objective assessments and improve clinical effectiveness. However, there is a lack of comprehensive analyses on the research trajectory, key directions, current trends, and future perspectives in this field. This research aims to comprehensively update the progress of AI in TCM over the past 24 years, based on data from the Web of Science database covering January 1, 2000, to March 1, 2024. Using advanced analytical tools, we conducted detailed bibliometric and visual analyses. The results highlight China's predominant influence, contributing 54.35 % of the total publications and playing a key role in shaping research in this field. Significant productivity was observed at institutions such as the China Academy of Chinese Medical Sciences, Beijing University of Chinese Medicine, and Shanghai University of Traditional Chinese Medicine, with Wang Yu being the most prolific contributor. The journal
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What OpenQuestion 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 OpenQuestion graph.