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DOI 10.2196/87163

Медицинская библиотека · JMIR formative research · 2026

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

Beste Nedim, Dratsch Thomas, Kottlors Jonathan, Floßdorf Pia, Konitsioti Agni-Maria, Volz Lukas J, Hanning Uta, Pinto Dos Santos Daniel, Goertz Lukas, Zopfs David, Kabbasch Christoph, Schlamann Marc, Laukamp Kai Roman, Schönfeld Michael

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

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.

Для цитирования: Beste Nedim, Dratsch Thomas, Kottlors Jonathan, Floßdorf Pia, Konitsioti Agni-Maria, Volz Lukas J, Hanning Uta, Pinto Dos Santos Daniel, Goertz Lukas, Zopfs David, Kabbasch Christoph, Schlamann Marc, Laukamp Kai Roman, Schönfeld Michael. Symptom-Only Localization of Brainstem Ischemia Using Large Language Models Versus Neurologists in Diffusion-Weighted Imaging-Positive Cases: Retrospective Single-Center Study.. JMIR formative research. 2026. DOI: 10.2196/87163.

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