ArticleiScience2025
A systematic review of computer-aided acupoint localization.
Article in iScience, 2025. 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
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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.
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Who cites it
2 citing papers in PubMed.
- AI-enabled comprehensive patient safety management in acupuncture: from risk identification to continuous quality improvement.Frontiers in medicine · 2026Review
- A Wearable System for Knee Osteoarthritis: Based on Multimodal Physiological Signal Assessment and Intelligent Rehabilitation.Sensors (Basel, Switzerland) · 2025Article
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
Acupuncture, as a traditional medical therapy, has gained widespread application globally. However, traditional acupoint localization has relied heavily on the personal experience of clinicians. With the advancement of AI, especially deep learning, researchers have begun exploring automated acupoint localization in recent years. This study systematically reviews the literature on acupoint localization in different anatomical regions using traditional algorithms and deep learning to evaluate the effectiveness, limitations, and future directions of these technologies. Drawing insights from the surveyed studies, we anticipate that the challenges outlined in this research will be addressed in the near future. As these challenges are overcome, automated acupoint localization techniques are expected to yield more promising outcomes. We hope our efforts will inspire further exploration of advanced AI methods for acupoint localization.
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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.