Evidence map›Paper›PMID 40176158›Full record

ArticleArthroplasty (London, England)2025

Editorial: Advances in artificial intelligence and robotics in joint arthroplasty.

Andrew P Kurmis, Sébastien Lustig, Francesco Zambianchi, Yunsu Chen

Abstract readEditorial
In one paragraph

Article in Arthroplasty (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
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

4 authors.

Andrew P KurmisCollege of Medicine & Public Health, Flinders University, Bedford Park, SA, 5042, Australia. andrew@kurmisorthopaedics.com.au.
Sébastien LustigOrthopaedic Surgery and Sports Medicine Department, Croix-Rousse Hospital, Lyon University Hospital, 69317, Lyon, France.
Francesco ZambianchiDepartment of Orthopaedic Surgery, Azienda Ospedaliero-Universitaria Di Modena, University of Modena and Reggio-Emilia, 41125, Modena, Italy.
Yunsu ChenDepartment of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai, 200233, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recently-completed special edition of Arthroplasty entitled 'Advances in Artificial Intelligence and Robotics in Joint Arthroplasty', brings together novel and innovative research from around the world in this cutting edge topic area. With robotics, artificial intelligence and technology-assistance (inside and out of the operating theatre) all becoming increasingly relevant to contemporary practice, we hope the readership will find this special edition an informative and thought-provoking read. Ultimately twelve individual papers were accepted for the edition, covering a range of exciting and novel applications. There clearly exists an ongoing need to provide further validation of new applications and, in many instances, replication of results away from designer sites is needed to provide robust generalizability of use. While several of the included papers show wide international collaboration, the prospect of future interactive work engaging leaders and think-tanks regionally and globally provides a tantalizing opportunity. With global health settings under increasing pressure and scrutiny to provide greater provision of joint replacement services - with the expectation of even more predictable (better) outcomes in a cost and resource efficacious manner - necessity will continue to drive further work exploring how technology-incorporation into arthroplasty care pathways might help address many of these considerations. There are undoubtedly exciting times ahead.

Indexed as

Artificial intelligenceCPAKMachine learningRoboticsTechnology-assisted surgery

Identifiers

PMID40176158
PMCPMC11966888

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.