Large language models in neuroradiology: an international survey of awareness, applications, and concerns.

IF 2.8 3区医学 Q2 CLINICAL NEUROLOGY

Neuroradiology Pub Date : 2026-09-05 DOI:10.1007/s00234-026-04176-5

Pranjal Rai, Neetu Soni, Manish Ora, Blake A Kassmeyer, Victoria M Silvera, Amit Agarwal, David F Black, Rajan Jain, Max Wintermark, Girish Bathla

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引用次数: 0

Abstract

Objectives: Large language models (LLMs) are increasingly used in medicine and research, but neuroradiologists' awareness, perceived utility, and concerns about integrity and disclosure remain incompletely characterized. This survey aimed to assess radiologists' awareness and perceptions of LLMs in clinical and research domains.

Materials and methods: An anonymous, voluntary SurveyMonkey survey was distributed internationally (October 1, 2024 to March 31, 2025) via neuroradiology society newsletters/membership channels and social media. Categorical variables were summarized as counts and percentages; Likert items were summarized using weighted means and response distributions. Item-level complete-case denominators were reported. Prespecified subgroup analyses used chi-square/Fisher exact tests (categorical) and nonparametric tests (ordinal), with Holm multiplicity control within prespecified multi-item question blocks and within each subgroup factor.

Results: A total of 265 respondents started the survey; after exclusions, 209 were included in the analytic sample, of whom 64.6% were male. Awareness of LLMs was high (ChatGPT: 83.3%), but knowledge gaps persisted (14.8% unfamiliar with all listed models; 16.7% misclassified DALL·E as an LLM). Respondents most frequently endorsed bounded, workflow-adjacent clinical applications, including guideline-based recommendations (75.6%) and protocol selection (60.8%), with lower endorsement for image interpretation (22.0%). Concerns were common regarding plagiarism/data fabrication (82.1%), inaccurate or biased outputs (75.8%), and accountability (72.1%), alongside support for AI-detection tools (77.0%) and documentation of LLM use aligned with an example journal policy (73.1%).

Conclusion: Survey respondents reported cautious optimism toward LLM integration, favoring workflow-adjacent applications while emphasizing disclosure, oversight, and targeted education.

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神经放射学中的大型语言模型：一项关于意识、应用和关注的国际调查。

目的：大型语言模型（llm）越来越多地用于医学和研究，但神经放射学家的意识，感知效用，以及对完整性和披露的关注仍然没有完全表征。这项调查旨在评估放射科医生在临床和研究领域对法学硕士的认识和看法。材料和方法：通过神经放射学会通讯/会员渠道和社交媒体，在全球范围内（2024年10月1日至2025年3月31日）进行了一项匿名、自愿的SurveyMonkey调查。分类变量汇总为计数和百分比；李克特项目采用加权均值和反应分布进行汇总。报告了项目级完整病例分母。预先指定的子组分析使用卡方/Fisher精确检验（分类）和非参数检验（序数），在预先指定的多项目问题块和每个子组因素中使用Holm多重性控制。结果：共有265名受访者开始调查；排除后纳入分析样本209例，其中男性64.6%。法学硕士的认知度很高（ChatGPT: 83.3%），但知识差距仍然存在（14.8%的人不熟悉所有列出的模型；16.7%的人错误地将DALL·E分类为法学硕士）。受访者最常支持有限的、与工作流程相关的临床应用，包括基于指南的建议（75.6%）和方案选择（60.8%），对图像解释的支持较低（22.0%）。常见的担忧是抄袭/数据伪造（82.1%），不准确或有偏见的输出（75.8%），问责制（72.1%），以及对人工智能检测工具的支持（77.0%）和法学硕士使用与示例期刊政策一致的文档（73.1%）。结论：调查受访者对法学硕士集成持谨慎乐观态度，在强调披露、监督和有针对性的教育的同时，更倾向于工作流程相关的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neuroradiology 医学-核医学

CiteScore

5.30

自引率

3.60%

发文量

214

审稿时长

4-8 weeks

期刊介绍： Neuroradiology aims to provide state-of-the-art medical and scientific information in the fields of Neuroradiology, Neurosciences, Neurology, Psychiatry, Neurosurgery, and related medical specialities. Neuroradiology as the official Journal of the European Society of Neuroradiology receives submissions from all parts of the world and publishes peer-reviewed original research, comprehensive reviews, educational papers, opinion papers, and short reports on exceptional clinical observations and new technical developments in the field of Neuroimaging and Neurointervention. The journal has subsections for Diagnostic and Interventional Neuroradiology, Advanced Neuroimaging, Paediatric Neuroradiology, Head-Neck-ENT Radiology, Spine Neuroradiology, and for submissions from Japan. Neuroradiology aims to provide new knowledge about and insights into the function and pathology of the human nervous system that may help to better diagnose and treat nervous system diseases. Neuroradiology is a member of the Committee on Publication Ethics (COPE) and follows the COPE core practices. Neuroradiology prefers articles that are free of bias, self-critical regarding limitations, transparent and clear in describing study participants, methods, and statistics, and short in presenting results. Before peer-review all submissions are automatically checked by iThenticate to assess for potential overlap in prior publication.