Clinical Report: Evaluating AI in Orthopaedic Clinical Decision-Making
Overview
This study evaluates the performance of next-generation large language models (LLMs) in orthopaedic clinical decision-making, comparing ChatGPT-5, Grok-3, and Gemini 2.5 Flash against human clinician consensus.
Background
The integration of large language models (LLMs) into clinical medicine has shown promise in various specialties, yet their effectiveness in orthopaedics remains uncertain. Previous studies have highlighted inconsistencies in AI performance, including factual inaccuracies and contextual reasoning gaps.
Data Highlights
This study utilized a dataset of 97 clinical cases from the OrthoBullets platform, assessing the alignment of AI responses with clinician consensus.
Key Findings
The study compared the performance of ChatGPT-5, Grok-3, and Gemini 2.5 Flash against pooled clinician responses.
AI responses were evaluated for alignment with the most popular clinician responses.
Previous evaluations indicated variability in AI performance across different clinical scenarios.
Standardized benchmarks for evaluating AI in clinical settings are lacking.
Longitudinal analysis allows for tracking changes in AI performance over time.
Clinical Implications
Clinicians should remain aware of the limitations and variability in AI performance when integrating these tools into practice.
Conclusion
This study highlights the evolving landscape of AI in orthopaedic decision-making.
