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Evaluating chat generative pre-trained transformer responses to common patient questions on temporomandibular
Bayram Sonmez Unuvar1, Elif Esra Ozmen Unuvar2
1KTO Karatay University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation - Konya, Türkiye.
Objective:
Temporomandibular disorders are among the most common causes of orofacial pain, often leading patients to seek information online. The increasing use of large language models such as chat generative pre-trained transformer in healthcare communication has raised questions about the reliability and readability of artificial intelligence-generated patient information. The aim of this study was to evaluate the accuracy, comprehensiveness, readability, and inter-rater reliability of chat generative pre-trained transformer-generated responses to common patient questions regarding temporomandibular disorders.
Methods:
ChatGPT (version 4.0) was prompted to generate 50 potential patient questions about temporomandibular disorders. Ten representative questions were selected and independently evaluated by five experts (two oral and maxillofacial surgeons, two physiotherapists, and one physical medicine specialist). Responses were rated using a four-point quality scale assessing accuracy and completeness. Readability was calculated using the Flesch-Kincaid method, and inter-rater reliability was assessed using the Intraclass Correlation Coefficient.
Results:
The responses demonstrated variable but generally acceptable quality. The overall Intraclass Correlation Coefficient value was 0.862, indicating good inter-rater agreement. Readability levels ranged from grade 6.2-10.7 (mean 8.0), corresponding to middle-to-high school comprehension. While most responses were rated satisfactory, several lacked sufficient clinical detail, particularly in differentiating professional consultation pathways.
Conclusion:
chat generative pre-trained transformer provides moderately reliable and readable information about temporomandibular disorders, supporting its potential role in patient education. However, reliance on artificial intelligence-generated frequently asked questions introduces methodological limitations and authority bias. Future studies should incorporate real patient data and external fact-checking to enhance clinical relevance.
