Improving ChatGPT's Performance in Orthopedics: Opportunities Using the CRISPE Framework.

Area of Science:

• Artificial Intelligence in Medicine
• Orthopedic Research

Background:

• ChatGPT is increasingly utilized in clinical settings, education, and research, including orthopedics.
• Concerns exist regarding ChatGPT's accuracy in orthopedic clinical decision-making, with reported rates varying widely (33%-80%).
• Inaccurate AI-generated responses pose risks to healthcare professionals and patients if perceived as credible and acted upon.

Purpose of the Study:

• To address the critical limitation of unstructured prompt engineering in orthopedic research concerning ChatGPT.
• To introduce and apply the CRISPE (Capacity/Role, Insight, Statement, Personality, Experiment) framework for systematic prompt refinement.
• To highlight opportunities for optimizing ChatGPT prompts within the field of orthopedics.

Main Methods:

• Application of the CRISPE framework to analyze recent orthopedic research involving ChatGPT.
• Systematic evaluation of prompt engineering strategies to improve ChatGPT's performance.
• Literature review focusing on AI accuracy and prompt optimization in medical research.

Main Results:

• The CRISPE framework provides a structured method to enhance prompt engineering for AI models like ChatGPT.
• Optimized prompts have the potential to significantly improve the accuracy and reliability of ChatGPT's responses in orthopedic contexts.
• Identified specific opportunities for prompt refinement in current orthopedic research.

Conclusions:

• Structured prompt engineering, exemplified by the CRISPE framework, is essential for improving ChatGPT's utility in orthopedics.
• Further research is necessary to validate and refine prompt engineering tools for AI in orthopedic practice and education.
• Optimized AI tools can enhance accuracy and reliability, serving as valuable assets in orthopedic research, education, and clinical practice.