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Integrating large language models into radiological practice
Marly van Assen1, Emanuele Muscogiuri2, Carlo N De Cecco2
1Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, GA, USA.
Purpose:
To highlight the potential of large language models (LLMs) in radiology and to stimulate discussion on their integration into clinical practice, including associated benefits and challenges.
Methods:
This editorial was authored with the assistance of an advanced artificial intelligence (AI) language model (ChatGPT-4, OpenAI), under direct human oversight. The content was critically reviewed, edited, and refined by the author, and all references were verified by the editors.
Results:
LLMs demonstrate substantial potential to enhance radiological practice, including improvements in workflow efficiency, reporting, education, and clinical decision support. However, their implementation raises important considerations related to accuracy, reliability, ethical use, and appropriate clinical oversight.
Conclusion:
The integration of LLMs into radiology requires a balanced approach that embraces their innovative capabilities while ensuring ethical, safe, and practical implementation. With appropriate safeguards and continued evaluation, LLMs may contribute to improved patient care and outcomes.
