Optimizing Large Language Models in Radiology and Mitigating Pitfalls: Prompt Engineering and Fine-tuning.

Area of Science:

• Artificial Intelligence in Medicine
• Medical Imaging Informatics

Background:

• Large language models (LLMs), including generative pretrained transformers (GPTs), are increasingly explored for medical and radiology applications.
• Understanding LLMs is essential for healthcare professionals due to their societal impact and growing integration into clinical workflows.

Purpose of the Study:

• To present techniques for optimizing LLMs for medical and radiology use cases.
• To describe the challenges and limitations associated with implementing LLMs in healthcare.
• To provide radiologists with foundational knowledge of LLM technology and best practices for their application.

Main Methods:

• Exploration of prompt engineering techniques to enhance LLM response accuracy and desirability.
• Explanation of fine-tuning processes to adapt general LLMs for specific medical tasks, such as clinical note summarization.
• Review of current proof-of-concept applications of LLMs in radiology literature.

Main Results:

• Prompt engineering and fine-tuning are key methods for improving LLM reliability and relevance in medical contexts.
• LLMs present unique challenges in healthcare, including probabilistic outputs, "hallucinations," biases, and security risks.
• Current LLM applications in radiology, such as decision support and report generation, are primarily proof-of-concept due to existing limitations.

Conclusions:

• LLMs have significant potential in radiology, but their probabilistic and complex nature necessitates careful optimization and understanding.
• Addressing challenges like hallucinations, bias, and reliability is critical for the widespread adoption of LLMs in medicine.
• Radiologists require baseline knowledge of LLM technology, prompt engineering, and fine-tuning to effectively and safely utilize these tools.