[Large language models from OpenAI, Google, Meta, X and Co. : The role of "closed" and "open" models in radiology]. / Große Sprachmodelle von OpenAI, Google, Meta, X und Co. : Die Rolle von "closed" und "open" Modellen in der Radiologie.

ABSTRACT

BACKGROUND: In 2023, the release of ChatGPT triggered an artificial intelligence (AI) boom. The underlying large language models (LLM) of the nonprofit organization "OpenAI" are not freely available under open-source licenses, which does not allow on-site implementation inside secure clinic networks. However, efforts are being made by open-source communities, start-ups and large tech companies to democratize the use of LLMs. This opens up the possibility of using LLMs in a data protection-compliant manner and even adapting them to our own data. OBJECTIVES: This paper aims to explain the potential of privacy-compliant local LLMs for radiology and to provide insights into the "open" versus "closed" dynamics of the currently rapidly developing field of AI. MATERIALS AND METHODS: PubMed search for radiology articles with LLMs and subjective selection of references in the sense of a narrative key topic article. RESULTS: Various stakeholders, including large tech companies such as Meta, Google and X, but also European start-ups such as Mistral AI, contribute to the democratization of LLMs by publishing the models (open weights) or by publishing the model and source code (open source). Their performance is lower than current "closed" LLMs, such as GPT­4 from OpenAI. CONCLUSION: Despite differences in performance, open and thus locally implementable LLMs show great promise for improving the efficiency and quality of diagnostic reporting as well as interaction with patients and enable retrospective extraction of diagnostic information for secondary use of clinical free-text databases for research, teaching or clinical application.