RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance

• URL: http://arxiv.org/abs/2311.18681v1
• Date: Thu, 30 Nov 2023 16:28:40 GMT
• Title: RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance
• Authors: Chantal Pellegrini, Ege \"Ozsoy, Benjamin Busam, Nassir Navab, Matthias Keicher
• Abstract summary: Conversational AI tools can generate and discuss clinically correct radiology reports for a given medical image. RaDialog is the first thoroughly evaluated and publicly available large vision-language model for radiology report generation and interactive dialog. 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.
• Score: 53.20640629352422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. RaDialog effectively integrates visual image features and structured pathology findings with a large language model (LLM) while simultaneously adapting it to a specialized domain using parameter-efficient fine-tuning. 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. 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. Our code is available on github: https://github.com/ChantalMP/RaDialog.

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