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Best Practices for Large Language Models in Radiology.

Christian Bluethgen¹, Dave Van Veen¹, Cyril Zakka¹

¹From the Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, Calif (C.B., D.V.V., C.P.L., S.G., A.C.); Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8005 Zurich, Switzerland (C.B., T.F.); Department of Electrical Engineering, Stanford University, Stanford, Calif (D.V.V.); Department of Cardiothoracic Surgery, Stanford Medicine, Stanford, Calif (C.Z.); Department of Medical Education, Icahn School of Medicine at Mount Sinai, New York, NY (K.E.L.); NVIDIA, New York, NY (K.E.L.); UT Health San Antonio, San Antonio, Tex (A.H.F.); Department of Biomedical Data Science, Stanford Medicine, Stanford, Calif (A.H.F., R.D., C.P.L., A.C.); Department of Dermatology, Stanford Medicine, Redwood City, Calif (R.D.); Department of Medicine, Stanford Medicine, Stanford, Calif (C.P.L., A.C.); and Department of Radiology, Stanford University, Stanford, Calif (C.P.L., S.G., A.C.).

|April 29, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) can enhance radiology by improving text data management and interpretation. This review guides radiologists on leveraging LLMs effectively, covering limitations and best practices for implementation.

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Area of Science:

Medical Imaging and Artificial Intelligence
Natural Language Processing in Healthcare

Background:

Radiologists integrate complex imaging data with clinical information for actionable insights.
Effective communication and data management are critical in radiology practices.
Vast amounts of text data in radiology present interpretation and management challenges.

Purpose of the Study:

To explore the potential of large language models (LLMs) in radiology.
To provide radiologists with insights into LLM foundations and strategic implementation.
To offer practical advice on optimizing LLM use in radiology practices.

Main Methods:

Review of current large language model capabilities in specialized tasks.
Analysis of LLM potential for managing and interpreting text data in radiology.
Expert insights from practical radiology and machine learning.

Main Results:

LLMs demonstrate impressive capabilities in specialized tasks without specific training.
Understanding LLM foundations and navigating their idiosyncrasies is key to unlocking potential.
Practical advice on limitations, prompting, and fine-tuning strategies is provided.

Conclusions:

LLMs offer significant opportunities to enhance radiology workflows.
Strategic implementation and understanding LLM characteristics are crucial for success.
This review equips radiologists with knowledge for effective LLM adoption.