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

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.