RadChat: A Radiology Chatbot Incorporating Clinical Context for Radiological Reports Summarization

Xin Mei, Libin Yang, Dehong Gao, Xiaoyan Cai, Tianming Liu, Junwei Han

Abstract: Radiological Report Summarization (RRS) involves automated summarization of key impressions derived from identified findings, intending to alleviate the workload and stress experienced by radiologists. Many existing RRS methods predominantly concentrate on summarizing findings, neglecting crucial clinical context, such as the patient’s previous medical examinations. This context, which is a focal point for radiologists, plays a critical role in producing comprehensive and accurate impressions. This paper endeavors to emulate the workflows of radiologists by incorporating the patient’s clinical context alongside current findings. To achieve this, we reconceptualize RRS as a conversational question-answering task, generating temporal radiological conversations. These conversations are subsequently employed to fine-tune a large chat model. The resulting radiology chatbot, RadChat, demonstrates superior performance in RRS task, showcasing the potential of integrating clinical context for more accurate impressions. Experimental results conducted on the MIMIC-CXR dataset validate the superiority of RadChat in comparison to state-of-the-art baselines.