Evidence map›Paper›PMID 39538068›Full record

ReviewJapanese journal of radiology2025

Artificial intelligence in fracture detection on radiographs: a literature review.

Antonio Lo Mastro, Enrico Grassi, Daniela Berritto, Anna Russo, Alfonso Reginelli, Egidio Guerra, Francesca Grassi, Francesco Boccia

Abstract readReview
PubMed Publisher
In one paragraph

Review in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Artificial superintelligence alignment in healthcare.Japanese journal of radiology · 2026
    Review
  5. Review
  6. Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Review
  7. Article
  8. Review
  9. Review
  10. Review
  11. 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

8 authors.

Antonio Lo MastroDepartment of Radiology, University of Campania "Luigi Vanvitelli", Naples, Italy. antonio.lomastro@unicampania.it.ORCID http://orcid.org/0009-0004-5038-5268
Enrico GrassiDepartment of Orthopaedics, University of Florence, Florence, Italy.
Daniela BerrittoDepartment of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy.
Anna RussoDepartment of Radiology, University of Campania "Luigi Vanvitelli", Naples, Italy.
Alfonso ReginelliDepartment of Radiology, University of Campania "Luigi Vanvitelli", Naples, Italy.
Egidio GuerraEmergency Radiology Department, "Policlinico Riuniti Di Foggia", Foggia, Italy.
Francesca GrassiDepartment of Radiology, University of Campania "Luigi Vanvitelli", Naples, Italy.
Francesco BocciaDepartment of Radiology, University of Campania "Luigi Vanvitelli", Naples, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

Indexed as

Artificial IntelligenceFractures, BoneRadiographic Image Interpretation, Computer-AssistedRadiographyAlgorithmsHumansSensitivity and SpecificityArtificial intelligenceDeep learningFracture detectionMachine learningMusculoskeletal imagingRadiomics

Identifiers

PMID39538068

What Socratic 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 Socratic graph.