Open-source large language models in radiology: 3 things to know

Commercial large language models, such as OpenAI’s GPT-4 and Google’s Gemini, have been more widely adopted in radiology compared to open-source models, according to an article published Jan. 28 in Radiology

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Baltimore-based University of Maryland School of Medicine researchers wrote the article to offer guidance on how to implement open-source large language models to radiology departments.

 Here are three things to know from the guidance:

  1. Researchers said there are advantages and disadvantages to using open-source models, such as Llama 3 and LLaVA-Med, in a radiology setting.

    Advantages include increased customization, reduced costs, smaller computational requirements and enhanced data security.

    Open-source models are still working to “close the gap” on performance and safety metrics, researchers said of potential disadvantages to not using commercial LLMs.

  2. The article offers instructions on how to select, load and deploy open-source modes within a radiology setting. It also provides implementation-ready code for different open-source tools.
  3. Clinical deployment considerations for using open-source LLMs include a lack of established best practices due to the rapid development of AI, as well as large language models’ tendency to exhibit biases and perpetuate misinformation, the researchers said. 

Read the full guidance here

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