ReviewResearch (Washington, D.C.)2026
Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.
Review in Research (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Salidroside alleviates TNF-α-induced endothelial inflammatory injury by modulating NF-κB/NLRP3 inflammasome-related signaling: an integrated network pharmacology and experimental study.Translational pediatrics · 2026Article
- A case analysis of Gitelman syndrome complicated with Sjögren's disease.Frontiers in medicine · 2026Article
- The NLRP3 inflammasome in gynecological cancers: a double-edged sword shaping the immune microenvironment and immunotherapy response.Frontiers in immunology · 2026Review
- AI-Powered Discovery of Rosmarinic Acid as a Novel Ferroptosis Inhibitor for Ulcerative Colitis via Targeting the ALOX15-VDAC1 Axis.Research (Washington, D.C.) · 2026Article
- Targeting regulatory T cell plasticity in gastric cancer: a unified bimodal model and precision immunotherapy paradigm: a hypothesis-generating review.Frontiers in oncology · 2026Article
- Advancing the frontiers of ovarian cancer therapy: a comprehensive synthesis of emerging cell death paradigms.Oncology reviews · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional Chinese medicine (TCM), rooted in holistic philosophy, features a "multicomponent, multitarget, multipathway" therapeutic model that has long posed challenges for modern scientific interpretation due to its inherent complexity. Studies elucidating its biological mechanisms have historically relied on correlation-based analytical paradigms. Although artificial intelligence (AI) has been increasingly introduced into TCM research, most current applications remain confined to disease classification, outcome prediction, or herb-target association mining, with limited capacity to reconstruct the underlying biological logic of Zheng (TCM Syndrome) differentiation and formula compatibility. This review systematically elaborates on how network pharmacology serves as a foundational framework, constructing "herb-compound-target-disease" networks that align with TCM's holistic nature, while AI addresses network pharmacology's limitations-machine learning streamlines active component screening and ADME/T (absorption, distribution, metabolism, excretion, and toxicity) property prediction, and deep learning decodes spectroscopic data, complex biological interaction networks, and formula synergies. The integrated "computational prediction-experimental validation" workflow, validated across oncology, metabolic diseases, and infectious diseases, has become the gold standard for mechanistic research. Additionally, AI revolutionizes TCM quality control by linking chemical signatures to stable efficacy, integrates multiomics data to construct holistic regulatory networks, and enables translational progress through precision patient stratification, real-world evidence integration, and TCM knowledge graphs that structure fragmented knowledge. With the advancement of technology, generative AI for drug design, large language models for mining ancient texts, and multimodal "life models" promise to deepen integration. Ultimately, AI transcends being a mere tool, translating TCM's holistic philosophy into modern scientific language, advancing its modernization and internationalization, and offering insights for multitarget drug development in global healthcare.
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.