ReviewFrontiers in medicine2026
AI-enabled comprehensive patient safety management in acupuncture: from risk identification to continuous quality improvement.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acupuncture, as an important component of traditional medicine, has been widely integrated into the management of various clinical conditions. Although acupuncture is generally considered safe and well tolerated, adverse events, including bleeding, pneumothorax, neurovascular injury, infection, and retained needles, may still occur. Current safety management approaches largely rely on operator experience and manual monitoring, which may be insufficient to address individual patient variability and dynamic procedural risks. In recent years, Artificial Intelligence (AI) has emerged as a promising technological approach to enhance patient safety management in acupuncture practice. This review focuses on the entire acupuncture care pathway and summarizes the potential applications of AI in risk identification, intelligent assistance, and safety monitoring. Current evidence suggests that AI technologies may support high-risk patient screening, personalized treatment planning, real-time procedural monitoring, and safety data management. However, existing evidence remains largely limited to technology development and preliminary validation, with insufficient clinical evidence. Future efforts should focus on establishing a human-AI collaborative framework to develop comprehensive intelligent patient safety management systems, facilitating the transition of acupuncture safety management from experience-driven practices toward data-informed decision-making and proactive risk prevention.
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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 OpenQuestion graph.