Limitations of knowledge competency and error patterns in large language models based on orthodontic licensing examinations

The superior performance of DS in standardized exams suggests potential for AI-assisted decision support in orthodontic training and licensing evaluation. However, the persistent factual errors and domain-specific limitations highlight the necessity for clinician verification in real-world applications. Integrating domain-specific knowledge refinement with logical reasoning modules could enhance LLMs' clinical utility in orthodontic practice.