Prompt Smarter, Not Harder: A Practical Guide to Prompt Engineering Techniques in Musculoskeletal Radiology

Purpose or Learning Objective:

1. Introduce radiologists to the fundamental concepts of prompt engineering in large language models for musculoskeletal-based tasks.

2. Explain key prompt techniques, including zero-shot, few-shot, chain-of-thought, tree-of-thought, and retrieval-augmented generation, with examples relevant to musculoskeletal radiology.

3. Highlight potential applications of prompt engineering in decision support, reporting automation, and clinical education for musculoskeletal radiologists.

Methods or Background: The integration of large language models into radiology has led to increased interest in optimizing their outputs through prompt engineering. Prompt design can significantly influence model performance, particularly in musculoskeletal radiology, where nuanced interpretation of findings and structured reporting are essential. Various prompting techniques have been developed to enhance context awareness, reasoning ability, and accuracy of responses generated by artificial intelligence. However, the optimal approach for different clinical tasks remains unclear. Understanding zero-shot, few-shot, chain-of-thought, tree-of-thought, and retrieval-augmented generation prompting is crucial for radiologists looking to leverage artificial intelligence effectively in musculoskeletal imaging.

Results or Findings: Each prompting technique has distinct advantages and limitations in musculoskeletal radiology applications:

• Zero-shot prompting: Large language models generate responses without prior examples. Although useful for broad queries, it may lack specificity for musculoskeletal radiology. Example: “Describe the magnetic resonance imaging features of Achilles tendinopathy.”

• Few-shot prompting: Provides the model with a few labeled examples to improve accuracy. Example: “Given these cases of osteoid osteoma, classify the next case.”

• Chain-of-thought prompting: Encourages stepwise reasoning, improving complex tasks. Example: “What is the best magnetic resonance imaging protocol for knee imaging? Let's think step by step.”

• Tree-of-thought prompting: Extends chain-of-thought by considering multiple branching possibilities, useful for generation of differential diagnosis.

• Retrieval-augmented generation: Large language models access external databases or guidelines to enhance responses. Example: “Using the provided context, summarize the European Society of Musculoskeletal Radiology guidelines on magnetic resonance imaging reporting of stress fractures.”

Conclusion: Prompt engineering offers a practical and scalable approach to optimize large language models for musculoskeletal radiology. Techniques such as few-shot and chain-of-thought prompting may enhance clinical reasoning. Approaches based on retrieval-augmented generation can help reduce falsified information by grounding responses in verified sources. Radiologists should tailor prompting strategies based on the specific clinical, educational, or research task. As artificial intelligence continues to evolve, prompt engineering will be a key competency for musculoskeletal radiologists integrating large language models into their workflow.

Publication History

Article published online:
02 June 2025