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Performance of large language artificial intelligence models on solving restorative dentistry and endodontics student
Paul Künzle1, Sebastian Paris2
1Department of Operative, Preventive and Pediatric Dentistry, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin, 14197, Germany. paul.kuenzle@charite.de.
Objectives:
The advent of artificial intelligence (AI) and large language model (LLM)-based AI applications (LLMAs) has tremendous implications for our society. This study analyzed the performance of LLMAs on solving restorative dentistry and endodontics (RDE) student assessment questions.
Materials And Methods:
151 questions from a RDE question pool were prepared for prompting using LLMAs from OpenAI (ChatGPT-3.5,-4.0 and -4.0o) and Google (Gemini 1.0). Multiple-choice questions were sorted into four question subcategories, entered into LLMAs and answers recorded for analysis. P-value and chi-square statistical analyses were performed using Python 3.9.16.
Results:
The total answer accuracy of ChatGPT-4.0o was the highest, followed by ChatGPT-4.0, Gemini 1.0 and ChatGPT-3.5 (72%, 62%, 44% and 25%, respectively) with significant differences between all LLMAs except GPT-4.0 models. The performance on subcategories direct restorations and caries was the highest, followed by indirect restorations and endodontics.
Conclusions:
Overall, there are large performance differences among LLMAs. Only the ChatGPT-4 models achieved a success ratio that could be used with caution to support the dental academic curriculum.
Clinical Relevance:
While LLMAs could support clinicians to answer dental field-related questions, this capacity depends strongly on the employed model. The most performant model ChatGPT-4.0o achieved acceptable accuracy rates in some subject sub-categories analyzed.
