Evidence map›Paper›PMID 42878864›Full record

ReviewUnfallchirurgie (Heidelberg, Germany)2026

[Artificial intelligence in traumatological imaging : Evidence, integration into the clinical pathway and limits].

Philipp Locherer, Lukas Müller, Daniel Pinto Dos Santos

Abstract readEnglish AbstractReview
PubMed Publisher
In one paragraph

Review in Unfallchirurgie (Heidelberg, Germany), 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

3 authors.

Philipp LochererKlinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland. philipp.locherer@unimedizin-mainz.de.
Lukas MüllerKlinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland.
Daniel Pinto Dos SantosKlinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly being integrated into clinical radiology workflows and has the potential to make a significant contribution to patient care in the future. Although there is currently insufficient evidence to enable a definitive assessment, it is nonetheless worthwhile to gain experience with AI software in the context of imaging in traumatology. While impressive performance can already be observed in clinical practice, users must remain aware of the regulatory framework and critically evaluate AI results with a vigilant eye.

Indexed as

Diagnostic imagingEvidence-based practiceRadiologyRegulatory frameworksSoftware

Identifiers

PMID42878864

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.