ReviewChinese medicine2026
Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration.
Review in Chinese medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Strategies for the source protection of data assets on processing techniques for intelligent manufacturing in traditional Chinese medicine.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Traditional Chinese Medicine (TCM) is a valuable medical treasure trove that not only demonstrated unique advantages in treating complex and refractory diseases but also left behind a rich legacy of ancient texts and valuable evidence-based medical data based on its human experience for future generations. Nevertheless, the extensive data within TCM has been plagued by challenges, including inadequate data standardization, inconsistent data quality, limited data structuring, and obstacles in interdisciplinary integration. Recent advancements in artificial intelligence (AI) techniques have markedly improved the efficiency and effectiveness with which multimodal data in TCM, including machine learning (ML), deep learning (DL), knowledge graphs (KG), and natural language processing (NLP), particularly large language models (LLMs). These advancements have facilitated more precise data analysis, enhanced clinical decision-making, and improved research outcomes in TCM, such as target discovery, virtual screening of natural products (NPs), symptom differentiation and auxiliary prescription. This article presents a comprehensive review of the progress in applying AI across four dimensions: multiscale data in TCM, TCM research and development, TCM diagnosis and treatment, and LLMs. In summary, the application of AI technology in the modernization of TCM is expected to motivate researchers to achieve a deeper understanding of state-of-the-art applications in data-driven TCM complex systems, fundamental scientific research, and precision medicine, thereby bringing more opportunities and innovations for the modernization of TCM.
Indexed as
Identifiers
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.