MGH Radiology Llama: A Llama 3 70B Model for Radiology
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916524503072768 |
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| author | Shi, Yucheng Shu, Peng Liu, Zhengliang Wu, Zihao Li, Quanzheng Liu, Tianming Liu, Ninghao Li, Xiang |
| author_facet | Shi, Yucheng Shu, Peng Liu, Zhengliang Wu, Zihao Li, Quanzheng Liu, Tianming Liu, Ninghao Li, Xiang |
| contents | In recent years, the field of radiology has increasingly harnessed the power of artificial intelligence (AI) to enhance diagnostic accuracy, streamline workflows, and improve patient care. Large language models (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 large language model: 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11848 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MGH Radiology Llama: A Llama 3 70B Model for Radiology Shi, Yucheng Shu, Peng Liu, Zhengliang Wu, Zihao Li, Quanzheng Liu, Tianming Liu, Ninghao Li, Xiang Computation and Language Artificial Intelligence In recent years, the field of radiology has increasingly harnessed the power of artificial intelligence (AI) to enhance diagnostic accuracy, streamline workflows, and improve patient care. Large language models (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 large language model: 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. |
| title | MGH Radiology Llama: A Llama 3 70B Model for Radiology |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.11848 |