Evidence map›Paper›PMID 41828026›Full record

ArticleDiagnostics (Basel, Switzerland)2026

Opportunities and Challenges of Visual Large Language Models in Imaging Diagnostics: Lessons from Brain Metastasis Detection in Clinical MRI.

Christian Nelles, Nour Abou Zeid, Robert Terzis, Andra-Iza Iuga, Lukas Görtz, Marvin A Spurek, David Maintz, Simon Lennartz, Jonathan Kottlors

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Christian NellesInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.ORCID 0000-0002-7351-7516
Nour Abou ZeidInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.
Robert TerzisInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.ORCID 0009-0007-1068-8477
Andra-Iza IugaInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.ORCID 0000-0002-3694-0235
Lukas GörtzInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.
Marvin A SpurekInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.ORCID 0000-0002-6129-9267
David MaintzInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.
Simon LennartzInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.ORCID 0000-0002-3254-4809
Jonathan KottlorsInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.ORCID 0000-0001-5021-6895

Funding

Else Kröner-Fresenius-Stiftung 2023_EKEA.77
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

brain metastasesdiagnostic accuracyMagnetic Resonance Imagingvisual large language models

Identifiers

PMID41828026
PMCPMC12984547

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.