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Radiology-GPT: A Large Language Model for Radiology

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arxiv 2306.08666 v2 pith:Y3VMDBP7 submitted 2023-06-14 cs.CL cs.AI

Zhengliang Liu , Aoxiao Zhong , Yiwei Li , Longtao Yang , Chao Ju , Zihao Wu , Chong Ma , Peng Shu
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Cheng Chen Sekeun Kim Haixing Dai Lin Zhao Lichao Sun Dajiang Zhu Jun Liu Wei Liu Dinggang Shen Xiang Li Quanzheng Li Tianming Liu
This is my paper · ORCID
classification cs.CLcs.AI
keywords radiology-gptlanguagemodelslargeradiologyfutureknowledgemodel
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce Radiology-GPT, a large language model for radiology. Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates superior performance compared to general language models such as StableLM, Dolly and LLaMA. It exhibits significant versatility in radiological diagnosis, research, and communication. This work serves as a catalyst for future developments in clinical NLP. The successful implementation of Radiology-GPT is indicative of the potential of localizing generative large language models, specifically tailored for distinctive medical specialties, while ensuring adherence to privacy standards such as HIPAA. The prospect of developing individualized, large-scale language models that cater to specific needs of various hospitals presents a promising direction. The fusion of conversational competence and domain-specific knowledge in these models is set to foster future development in healthcare AI. A demo of Radiology-GPT is available at https://huggingface.co/spaces/allen-eric/radiology-gpt.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RadPhi-3: Small Language Models for Radiology

    cs.CV 2024-11 conditional novelty 4.0 of 10

    RadPhi-3, a 3.8B parameter instruction-tuned model, handles radiology QA and chest X-ray report utilities and posts a marginal SOTA score on the RaLEs benchmark.

  2. Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation

    cs.CV 2024-12 conditional novelty 3.0 of 10

    A LLaVA-style radiology report generator using LoRA fine-tuning and stitched chest X-ray inputs placed fourth in the RRG24 shared task.

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