ArticleEuropean radiology2026
AI-driven diagnosis of vulnerable intracranial atherosclerotic plaques using large language models and vision transformers: a multi-center study.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
objectivesHigh-resolution vessel wall imaging (HR-VWI) is essential for diagnosing vulnerable intracranial atherosclerotic plaques, but its interpretation requires expertise. This study investigates the integration of large language models (LLMs) and deep learning (DL) for more efficient diagnosis. MATERIALS AND
methodsA retrospective study of symptomatic intracranial atherosclerotic stenosis patients (June 2018-June 2024) was conducted. LLMs (ChatGPT-4o, DeepSeek-V3, and Moonshot AI) were trained on HR-VWI reports to extract diagnostic insights. Additionally, DL models, ResNet50 and Vision Transformer (ViT), were used to classify vulnerable plaques. Diagnostic accuracy, sensitivity, specificity, and time efficiency were evaluated with both junior and senior doctors.
resultsA total of 1806 plaques from 726 patients were analyzed. ChatGPT-4o exhibited the highest diagnostic performance (AUC: 0.874). Among DL models, ViT outperformed ResNet50 (AUC: 0.913 vs. 0.845). LLMs and ViT significantly improved junior doctors' diagnostic accuracy and reduced plaque assessment time (from 301 s to 174 s, p < 0.05).
conclusionThe integration of LLMs and DL models enhanced diagnostic performance and efficiency, especially for junior doctors. This approach could reduce the burden on healthcare systems, particularly in resource-limited settings, by improving diagnostic accuracy and reducing the time required for plaque analysis. KEY POINTS: Question How can AI models assist less experienced doctors in accurately identifying vulnerable intracranial plaques on high-resolution vessel wall imaging (HR-VWI) and improve diagnostic efficiency? Findings The integration of large language models (LLMs) and deep learning (ViT) significantly improves diagnostic accuracy and efficiency, reducing assessment time for vulnerable plaques. Clinical relevance This study demonstrates that combining LLMs and deep learning enables junior doctors to achieve near-expert accuracy in diagnosing vulnerable plaques, potentially reducing stroke risk and easing diagnostic burdens in resource-limited healthcare environments.
Indexed as
Identifiers
41123642What OpenQuestion holds
Registered trials
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.