Evidence map›Paper›PMID 36140636›Full record

ReviewDiagnostics (Basel, Switzerland)2022

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review.

Kenneth Chen, Christoph Stotter, Thomas Klestil, Stefan Nehrer

Open access · goldAbstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
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

30 citing papers in PubMed, 47 citations in OpenAlex.

  1. Artificial Intelligence Scribes in Orthopaedic Surgery: A Narrative Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
    Review
  2. Review
  3. Article
  4. Review
  5. FDA-Cleared Artificial Intelligence Medical Devices in Orthopaedic Surgery.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
    Article
  6. Review
  7. Artificial Intelligence Applications in Musculoskeletal Imaging.Current reviews in musculoskeletal medicine · 2025
    Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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 at 1 institution in 1 country.

Kenneth ChenDepartment for Orthopedics and Traumatology, Landesklinikum Baden-Mödling, 2340 Mödling, Austria.
Christoph StotterDepartment for Orthopedics and Traumatology, Landesklinikum Baden-Mödling, 2340 Mödling, Austria.
Thomas KlestilDepartment for Orthopedics and Traumatology, Landesklinikum Baden-Mödling, 2340 Mödling, Austria.
Stefan NehrerDepartment for Health Sciences, Medicine and Research, Danube University Krems, 3500 Krems, Austria.ORCID 0000-0001-8008-2226
Universität für Weiterbildung Krems · AT

Funding

Gesellschaft für Forschungsförderung, Niederösterreich m.b.H. (GFF) LS20-020.
6 · The paper itself

Abstract

Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.

Indexed as

artificial intelligencedeep learningmachine learningmusculoskeletal imagingradiographX-ray

Identifiers

PMID36140636
PMCPMC9498096
OpenAlexW4296251976

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.