Background This study explored the diagnostic accuracy of artificial intelligence (AI) chatbots and dental students
when responding to questions related to pulpal and periapical diseases. Rapid advancements in AI have led to
increased interest in their applicability to clinical education and decision-making in dentistry.
Objective To compare the accuracy rates of responses given by dental students and various AI-based chatbots
(ChatGPT-3.5, ChatGPT-4o, Gemini, and Microsoft Copilot) to multiple-choice questions designed to assess knowledge
related to pulpal and periapical diseases.
Methods The study included third- and fifth-year dental students representing different levels of clinical training,
along with four distinct AI-based chatbots. A total of 327 responses were collected from students, while each chatbot
generated 450 responses. The evaluation was based on 15 multiple-choice questions developed in accordance
with the 2020 version of the American Association of Endodontists (AAE) clinical guidelines. The accuracy rates of
the groups were compared using descriptive statistics, one-way ANOVA, Bonferroni post hoc tests for significant
differences, and Chi-square tests for correct versus incorrect response ratios.
Results The highest accuracy rate was observed among fifth-year dental students (85.1%), followed by ChatGPT-4o
(79.6%), ChatGPT-3.5 (75.1%), Gemini (71.6%), third-year students (64.9%), and Microsoft Copilot (61.3%). A statistically
significant difference was found among the groups (p < 0.05). ChatGPT-4o demonstrated a comparable accuracy rate
to fifth-year students with more clinical experience (p > 0.05), whereas other chatbots and third-year students showed
lower performance.
Conclusion Chatbots exhibited varying levels of accuracy in diagnosing pulpal and periapical diseases. ChatGPT-4o
performed at a level similar to that of more clinically experienced students, suggesting its potential as a supportive
tool in dental education and clinical decision support systems. However, the relatively lower accuracy rates of modelssuch as Gemini and Microsoft Copilot underscore the continued importance of human expertise. These findings
suggest that while AI systems may serve as complementary tools in education, they cannot fully replace clinical
judgment grounded in human experience.