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

Mark Vorensky1,2, Daniel Peredo2, Richard Ferraro1

  • 1Department of Rehabilitation and Movement Sciences, School of Health Professions, Rutgers University, Newark, NJ.

JOSPT Methods
|September 8, 2025
PubMed
Summary
This summary is machine-generated.

Improving ChatGPT accuracy in orthopedic research is crucial. The CRISPE framework offers a structured approach to prompt engineering, enhancing reliability for clinicians, trainees, and patients.

Keywords:
ChatGPTartificial intelligencelarge language modelmusculoskeletalnatural language processingorthopedics

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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.