RaDialog Instruct Dataset

Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology. Such a human-in-the-loop radiology assistant could facilitate a collaborative diagnostic process, thus saving time and improving the quality of reports. Towards this goal, we introduce RaDialog, the first thoroughly evaluated and publicly available large vision-language model for radiology report generation and interactive dialog. To keep the conversational abilities of the underlying LLM, we propose a comprehensive, semi-automatically labeled, image-grounded instruct dataset for chest X-ray radiology tasks. The dataset includes a variety of tasks, such as report correction, summarization or finding prediction. By training with this dataset, our method achieves state-of-the-art clinical correctness in report generation and shows impressive abilities in interactive tasks such as correcting reports and answering questions, serving as a foundational step toward clinical dialog systems.

会话式人工智能工具，能够基于给定医学图像生成和讨论临床正确的放射学报告，有望彻底改变放射学领域。此类人机协同的放射学助手能够促进协同诊断过程，从而节省时间并提升报告质量。为实现此目标，我们推出了RaDialog，这是首个经过全面评估且公开可用的针对放射学报告生成和交互式对话的大视觉-语言模型。为了保留底层大语言模型（LLM）的会话能力，我们提出了一套全面、半自动标注、基于图像的指令数据集，用于胸部X射线放射学任务。该数据集涵盖了多种任务，如报告校正、总结或预测寻找。通过使用此数据集进行训练，我们的方法在报告生成方面实现了业界领先的临床准确性，并在交互式任务如报告校正和回答问题等方面展现出令人印象深刻的技能，这为临床对话系统的构建奠定了基础。