Assessing large language models in radiation risk communication: susceptibility, cultural-linguistic effects, and ethical reasoning.

PURPOSE: To assess the extent to which large language models (LLMs) amplify or attenuate inaccurate or contested narratives in radiation contexts and to evaluate their potential influence on public risk perception, patient communication in radiotherapy, and radiation protection policy implementation.

MATERIALS AND

METHODS

We developed a structured framework to extract agreement and sentiment from LLMs. This was applied to OpenAI's GPT family of models to examine susceptibility to strong or misframed radiological opinions, cultural and linguistic bias on controversial radiological topics, and philosophical or moral alignment in radiation-related scenarios. Additionally, GPT-4o mini was used to analyze sentiment trends in the r/Radiation subreddit (February 2021-December 2023). A novel model, AntiRadiophobeGPT, was created to counter radiophobic and myth-driven narratives and evaluated against real user comments.

RESULTS AND

CONCLUSIONS

Smaller LLMs (e.g. GPT-4o mini) exhibited significantly higher risk assessment of potentially radiophobic statements than their larger counterparts in general domain radiological risk assessment questions and higher agreement with controversial expert domain questions. Use of Chinese-language prompts or models further increased bias toward culturally sensitive radiological topics. All tested models showed deontological tendencies in moral alignment, with variations across scenarios. Subreddit analysis indicated that health-related myths were most prevalent, but overall community-wide radiophobia and hostility declined over the 3 years. AntiRadiophobeGPT effectively addressed misconceptions with high factual accuracy and demonstrated significantly lower levels of radiophobia and antagonism compared to user-generated responses. These findings underscore the importance of careful LLM deployment in radiological contexts to avoid misinformation propagation and support effective science communication. Overall, this work bridges artificial intelligence and radiation biology by demonstrating how LLM-driven communication can influence radiation risk perception and inform radiological safety practices.