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Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision
Published on: April 29, 2014
Effect of artificial intelligence-assisted personalized feedback on radiographic diagnostic performance of dental
Busra Nur Gokkurt Yilmaz1, Furkan Ozbey2, Birkan Eyup Yilmaz3
1Giresun Oral and Dental Health Center, Department of Dentomaxillofacial Radiology, Giresun, Türkiye. busranur581@hotmail.com.
Background:
This study aimed to evaluate the impact of MeSH based personalized learning guides generated by ChatGPT-4o on the radiographic diagnostic performance of dental students and to compare it with the traditional correct/incorrect feedback method.
Methods:
This randomized controlled study was conducted among fifth-year dental students at Afyonkarahisar Health Sciences University. A total of 110 students were randomly assigned to either the experimental or control group. The experimental group received personalized study guides targeting their learning gaps, generated by ChatGPT-4o based on Medical Subject Headings (MeSH). The control group received only a standard correct/incorrect feedback analysis. One month after the intervention, a post-test was administered to assess diagnostic accuracy and student satisfaction.
Results:
The increase in test scores from pre- to post-test was significantly higher in the experimental group (3.6 ± 1.0) compared to the control group (1.3 ± 1.2; p < 0.001). Final test scores were also significantly higher in the experimental group (p < 0.001). Survey responses indicated that the experimental group rated the feedback as more understandable, beneficial, and motivating compared to the control group.
Conclusions:
ChatGPT-4o based personalized feedback proved to be an effective tool for enhancing diagnostic performance and supporting learning in dental education. The findings suggest that AI-driven individualized educational strategies hold significant potential in the future of dental training.
