Evidence map›Paper›PMID 42418842›Full record

ArticleJMIR formative research2026

Symptom-Only Localization of Brainstem Ischemia Using Large Language Models Versus Neurologists in Diffusion-Weighted Imaging-Positive Cases: Retrospective Single-Center Study.

Nedim Beste, Thomas Dratsch, Jonathan Kottlors, Pia Floßdorf, Agni-Maria Konitsioti, Lukas J Volz, Uta Hanning, Daniel Pinto Dos Santos, Lukas Goertz, David Zopfs and 4 more

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

14 authors.

Nedim BesteUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0001-5420-1199
Thomas DratschUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0003-4014-7763
Jonathan KottlorsUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0002-0475-1472
Pia FloßdorfUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0001-5453-1059
Agni-Maria KonitsiotiUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0002-7296-4349
Lukas J VolzUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0002-0161-654X
Uta HanningUniversity Medical Centre Mannheim, Mannheim, Germany.ORCID 0000-0002-7543-8555
Daniel Pinto Dos SantosJohannes Gutenberg University Mainz, Mainz, Germany.ORCID 0000-0003-4785-6394
Lukas GoertzUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0002-2620-7611
David ZopfsUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0001-9978-7453
Christoph KabbaschUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0003-3712-2258
Marc SchlamannUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0002-7734-611X
Kai Roman LaukampUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0002-5600-5914
Michael SchönfeldUniversity Hospital Cologne, Kerpener Str 62, Cologne, 50937, Germany, 49 2214780.ORCID 0000-0003-2096-3448

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Symptom-based localization of brainstem ischemia is challenging because of the anatomical complexity of the brainstem and the nonspecific overlap of clinical syndromes. Whether large language models (LLMs) can meaningfully assist in this task remains uncertain. Objective: This study aimed to compare the performance of several OpenAI LLMs and neurologists in localizing diffusion-weighted imaging (DWI)-confirmed brainstem ischemic lesions based on symptom descriptions alone. Methods: In this retrospective single-center study, 109 patients with DWI-confirmed acute brainstem ischemia were included. Three neurologists and 6 LLMs (GPT-5, GPT-4, GPT-4.1, GPT-4o, o3, and o3-pro) predicted lesion localization using a combined anatomical-lateral end point (left or right midbrain, pons, and medulla) based on symptom descriptions alone. Overall and regional accuracy, the Cohen κ, 6-class confusion matrices, and point-biserial correlations between symptom count and correct prediction were assessed. Because all raters evaluated the same cases, paired McNemar tests with Benjamini-Hochberg correction were used for pairwise performance comparisons. Results: GPT-4 and GPT-4o achieved the highest overall accuracy (61/109, 56%; 95% CI 46.1%-65.5%). Agreement with the DWI reference standard remained limited across all raters, with the Cohen κ reaching a maximum of 0.291 for GPT-4o. Confusion matrices showed that higher performance was driven mainly by pontine cases, whereas misclassification remained frequent in mesencephalic and medullary lesions. Regional analyses outside the pons were imprecise because mesencephalic and medullary subgroups each contained only 16 cases. A higher number of documented symptoms was associated with correct prediction for GPT-4, GPT-5, GPT-o3, and 1 neurologist. Conclusions: Although some LLMs showed higher relative accuracy than the participating neurologists, absolute performance remained limited and clinically insufficient. These findings are best interpreted as an exploratory benchmark under constrained conditions: absolute performance remained modest, agreement beyond chance was limited, and performance outside pontine lesions was inconclusive.

Indexed as

Brain IschemiaBrain StemDiffusion Magnetic Resonance ImagingNeurologistsAgedFemaleHumansLarge Language ModelsMaleMiddle AgedRetrospective StudiesAIartificial intelligencebrainstem ischemiadiagnostic accuracyGPT-4language modelsstroke localization

Identifiers

PMID42418842
PMCPMC13345501

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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.