ReviewEvidence-based complementary and alternative medicine : eCAM2021
Overview of Artificial Intelligence Applications in Chinese Medicine Therapy.
Review in Evidence-based complementary and alternative medicine : eCAM, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- DBN Neural Network Model Combined with Meta-Analysis on the Curative Effect of Acupuncture and Massage.Computational intelligence and neuroscience · 2022Pooled it
- Development and Deployment of DeepSeek-Based Applications in Healthcare: A Chinese Perspective.Health care science · 2026Article
- Acupoint massage: a comprehensive descriptive review of its forms, applications, and underlying mechanisms.Chinese medicine · 2025Review
- Developing a deep learning model for the automated monitoring of acupuncture needle insertion: enhancing safety in traditional acupuncture practices.BMC complementary medicine and therapies · 2025Article
- Data Ownership in the AI-Powered Integrative Health Care Landscape.JMIR medical informatics · 2024Article
- Review
- AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine.JMIR medical informatics · 2024Article
- Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare.Integrative medicine research · 2024Article
- The Modernization of Oriental Music Therapy: Five-Element Music Therapy Combined with Artificial Intelligence.Healthcare (Basel, Switzerland) · 2024Article
- GPT-4 can pass the Korean National Licensing Examination for Korean Medicine Doctors.PLOS digital health · 2023Article
- Evolution from Medical Imaging to Visualized Medicine.Advances in experimental medicine and biology · 2023Article
- Artificial intelligence-directed acupuncture: a review.Chinese medicine · 2022Review
- The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review.Frontiers in pharmacology · 2022Article
- Utilization of artificial intelligence in clinical practice: A systematic review of China's experiences.Digital healthReview
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
To evaluate the importance of AI technologies in modernizing traditional Chinese medicine (TCM) therapy, this article presents the systematic review of the relevant literature and explains the beneficial effects of AI technology on the TCM treatment outcomes from the experience of famous and veteran Chinese medicines, including acupuncture, Tui Na massage, and Qigong practitioners. This study also focuses on the urgent necessity to apply AI technologies to develop therapeutic models on the theme "treating the disease before it happens." Furthermore, the study also discusses the major bottlenecks and future prospects for the development of intelligent TCM treatment strategies.
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.