MGH Radiology Llama: A Llama 3 70B Model for Radiology (2408.11848v2)
Abstract: In recent years, the field of radiology has increasingly harnessed the power of AI to enhance diagnostic accuracy, streamline workflows, and improve patient care. LLMs have emerged as particularly promising tools, offering significant potential in assisting radiologists with report generation, clinical decision support, and patient communication. This paper presents an advanced radiology-focused LLM: MGH Radiology Llama. It is developed using the Llama 3 70B model, building upon previous domain-specific models like Radiology-GPT and Radiology-Llama2. Leveraging a unique and comprehensive dataset from Massachusetts General Hospital, comprising over 6.5 million de-identified medical reports across various imaging modalities, the model demonstrates significant improvements in generating accurate and clinically relevant radiology impressions given the corresponding findings. Our evaluation, incorporating both traditional metrics and a GPT-4-based assessment, highlights the enhanced performance of this work over general-purpose LLMs.
- Yucheng Shi (30 papers)
- Peng Shu (34 papers)
- Zhengliang Liu (91 papers)
- Zihao Wu (100 papers)
- Quanzheng Li (122 papers)
- Tianming Liu (161 papers)
- Ninghao Liu (98 papers)
- Xiang Li (1002 papers)