Evidence map›Paper›PMID 42729912›Full record

ReviewFrontiers in medicine2026

AI-enabled comprehensive patient safety management in acupuncture: from risk identification to continuous quality improvement.

Xixi Wei, Chao Niu

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xixi WeiHeji Hospital Affiliated to Changzhi Medical College, Changzhi, China.
Chao NiuChangzhi Hospital of Traditional Chinese Medicine, Changzhi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

acupunctureartificial intelligencedigital healthhuman–AI collaborationpatient safetyrisk management

Identifiers

PMID42729912
PMCPMC13563269

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.