Recent advances in artificial intelligence have expanded the use of large language models (LLMs) beyond speech-based applications and increased interest in their potential roles in dental education. However, evidence regarding LLM performance in postgraduate dental education, particularly in prosthodontics, remains limited. Therefore, this study aimed to compare the accuracy of responses from prosthodontic residents and LLMs to standardized multiple-choice questions in prosthodontics and to explore the potential role of artificial intelligence in prosthodontic education. Thirty-two prosthodontic residents participated in this cross-sectional study. Participants completed a standardized 30-item multiple-choice test comprising four demographic items and 26 questions assessing basic knowledge, general dentistry, and advanced prosthodontic specialty questions. The same questions were administered to seven large language models (LLMs): ChatGPT-4o, ChatGPT-o1, ChatGPT-o3-mini, Claude Sonnet 3.7, Gemini 2.5 Pro, Microsoft Copilot (web interface, accessed in August 2025), and DeepSeek V3. Response accuracy and consistency were evaluated. Statistical analyses were performed using IBM SPSS Statistics (version 27.0), with statistical significance set at p 0.05). In addition, no significant association was found between the duration of prosthodontic residency training and residents’ response accuracy (p > 0.05). LLMs achieved high scores on this structured MCQ-based assessment, particularly in advanced theoretical prosthodontic items. However, these findings should be interpreted with caution within the limits of a written examination format and do not represent overall clinical competence or real-world patient care performance. Accordingly, artificial intelligence may be considered a supportive educational tool in postgraduate prosthodontic education rather than a replacement for clinical training.
Ates et al. (Sun,) studied this question.