ReviewEvidence-based complementary and alternative medicine : eCAM2022
The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine.
Review in Evidence-based complementary and alternative medicine : eCAM, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it.
- Global research trends of TCM-based nonpharmacologic interventions in breast cancer supportive care: a bibliometric analysis (2000-2025).Frontiers in oncology · 2026Pooled it
- Student standardized patients versus occupational standardized patients for improving clinical competency among TCM medical students: a 3-year prospective randomized study.BMC medical education · 2023Trial
- Tuning and clinical application of large language models in Traditional Chinese Medicine: scoping review.Chinese medicine · 2026Review
- Artificial intelligence guided acupuncture decision making and treatment: a review of research.Frontiers in medicine · 2026Review
- A supported decision-making model for idiopathic pulmonary fibrosis based on feature screening and optimized neural network.Scientific reports · 2025Article
- Diagnostic Features and Prescription Rules of Influenza-Like Illnesses in Traditional Chinese Medicine: A Data Mining Approach.Journal of multidisciplinary healthcare · 2025Article
- Digital intelligence technology: new quality productivity for precision traditional Chinese medicine.Frontiers in pharmacology · 2025Review
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
With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China.
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.