Beyond Accuracy: Evaluating Reasoning and Reference Reliability in Orthopaedic Large Language Model Applications – Cureus

The article “Accuracy Is Not Enough: Reasoning and Reference Reliability in Orthopaedic Large Language Model (LLM) Applications” discusses the limitations of current LLMs in the field of orthopaedics. While these AI models display impressive accuracy in generating responses, their reasoning capabilities and the reliability of referenced information can be inadequate. This raises concerns regarding patient safety and clinical decision-making, as practitioners may rely on potentially flawed outputs. The study emphasizes the importance of not only ensuring high accuracy but also critically evaluating the reasoning processes of LLMs and the credibility of sources they cite. Recommendations include enhancing model training with high-quality, peer-reviewed literature and integrating expert feedback to improve both reasoning and reference reliability. By addressing these gaps, healthcare providers can better leverage LLMs in orthopaedic practice, ultimately leading to safer and more effective patient outcomes. This underscores the need for ongoing evaluation and improvement in AI applications within medicine.

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