Evidence map›Paper›PMID 39811638›Full record

ReviewiScience2025

Computer-aided robotics for applications in fracture reduction surgery: Advances, challenges, and opportunities.

Xianzheng Zhou, Yimiao Chen, Genyuan Miao, Yanchao Guo, Qinhe Zhang, Jianping Bi

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Observational
  3. Review
  4. Article
  5. An automated method for precise definition of fracture reduction targets.Medical & biological engineering & computing · 2026
    Article
  6. Article
  7. [Short-term effectiveness of visual treatment solution-assisted treatment for Crowe typeZhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026
    Article
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
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

6 authors.

Xianzheng ZhouSchool of Mechanical Engineering, Shandong University, Jinan 250061, P.R. China.
Yimiao ChenSchool of Mechanical Engineering, Shandong University, Jinan 250061, P.R. China.
Genyuan MiaoSchool of Mechanical Engineering, Shandong University, Jinan 250061, P.R. China.
Yanchao GuoSchool of Mechanical Engineering, Shandong University, Jinan 250061, P.R. China.
Qinhe ZhangSchool of Mechanical Engineering, Shandong University, Jinan 250061, P.R. China.
Jianping BiThe First Clinical College, Shandong University of Traditional Chinese Medicine, Jinan 250013, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advancement of information technology and AI has boosted global economic and social development. Robot systems (RS) and computer-aided technology (CAT) are used in various domains, including social production and human existence. Traditional fracture reduction surgery relies on the expertise and surgical skills of surgeons to realign fractures in patients. Researchers have developed robotic and assisted systems to automate fracture reduction surgery in recent decades. Computer-aided fracture reduction robot system (CARS) is used to replace the manual reduction performed by conventional physicians. A partial CARS has been used successfully in clinical fracture reduction surgery. This study provides an overview of CARS. First, the RS and CAT used in fracture reduction surgery are overviewed. Furthermore, a comprehensive analysis of CARS is presented, encompassing their design, experimental validation, and clinical applications, while highlighting recent advancements and potential future directions in this domain. The suggested CARS for fracture reduction are compared in different ways. The learning curve and technical ethics of CARS are summarized. The paper addresses unresolved research gaps and technical challenges, providing recommendations to guide future study.

Indexed as

Applied sciencesHealth sciencesNatural sciences

Identifiers

PMID39811638
PMCPMC11732504

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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.