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.