Benchmarking Different Natural Language Processing Models for Their Responses to Queries on Toothsupported Fixed Dental Prostheses in Terms of Accuracy and Consistency

Abstract

Aim: This study aimed to evaluate the accuracy and repeatability of responses generated by four different software programs regarding tooth-supported fixed dental prostheses. Materials and Method: Twelve open-ended questions in Turkish were created and posed to four different NLPs according to the following models: OpenAI o3 (LRM-O), OpenAI GPT 4.5 (LLM-G), DeepSeek R1 (LRM-R), and DeepSeek V3 (LLM-V) with pre-prompts in the morning, afternoon, and evening. The responses were evaluated with a holistic rubric. For accuracy assessments, the Kruskal–Wallis H test was used. Consistency between the graders’ responses was assessed using the Brennan and Prediger coefficient and the Cohen kappa coefficient. Repeatability was assessed using the Fleiss kappa and Krippendorff alpha coefficients (p < 0.05). Results: There was no statistically significant difference in accuracy between the LRM-O, LLM-G, LRM-R, and LLM-V groups (p = 0.298). The respective accuracies of LRM-O, LLM-G, LRM-R, and LLM-V were 77.7%, 50%, 66.6%, and 77.7%. In addition, the repeatability of LLMs was found to be almost perfect, whereas that of LRMs was substantial. Conclusion: Within the limitations of the study, LRMs and LLMs exhibited similar accuracy. However, the repeatability of LLMs was higher than that of LRMs. Keywords: Artificial intelligence, Dental prostheses, Treatment protocols

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