Performance of large language artificial intelligence models on solving restorative dentistry and endodontics student assessments.
Clin Oral Investig
; 28(11): 575, 2024 Oct 07.
Article
em En
| MEDLINE
| ID: mdl-39373739
ABSTRACT
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 ANDMETHODS:
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Endodontia
Limite:
Humans
Idioma:
En
Revista:
Clin Oral Investig
/
Clin. oral investig
/
Clinical oral investigations
Assunto da revista:
ODONTOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Alemanha
País de publicação:
Alemanha