标题： 可信的医学影像：大型语言模型在跨模态幻觉方面的研究 显示英文标题

标题： Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities

摘要： 大型语言模型（LLMs）越来越多地应用于医学影像任务，包括图像解释和合成图像生成。 然而，这些模型经常产生幻觉，即自信但错误的输出，可能会误导临床决策。 本研究从两个方向审视幻觉：图像到文本，其中LLMs从X光、CT或MRI扫描生成报告；文本到图像，其中模型从临床提示生成医学图像。 我们分析了事实不一致和解剖不准确等错误，并使用专家指导的标准在不同成像模式下评估输出。 我们的研究结果揭示了在解释性和生成性任务中常见的幻觉模式，对临床可靠性具有影响。 我们还讨论了导致这些失败的因素，包括模型架构和训练数据。 通过系统研究图像理解和生成，这项工作提供了改进LLM驱动的医学影像系统安全性和可信度的见解。 显示英文摘要

摘要： Large Language Models (LLMs) are increasingly applied to medical imaging tasks, including image interpretation and synthetic image generation. However, these models often produce hallucinations, which are confident but incorrect outputs that can mislead clinical decisions. This study examines hallucinations in two directions: image to text, where LLMs generate reports from X-ray, CT, or MRI scans, and text to image, where models create medical images from clinical prompts. We analyze errors such as factual inconsistencies and anatomical inaccuracies, evaluating outputs using expert informed criteria across imaging modalities. Our findings reveal common patterns of hallucination in both interpretive and generative tasks, with implications for clinical reliability. We also discuss factors contributing to these failures, including model architecture and training data. By systematically studying both image understanding and generation, this work provides insights into improving the safety and trustworthiness of LLM driven medical imaging systems.