Author Identifier (ORCID)
Haitham Khaled: https://orcid.org/0000-0001-5865-3686
Abstract
Introduction: Artificial intelligence is emerging in dental education, but its use in preclinical assessment remains limited. Large language models like ChatGPT® V4.5 enable non-programmers to build AI models through real-time guidance, addressing the coding barrier. Aim: This study aims to empower clinician-led, low-cost, AI-driven assessment models in preclinical restorative dentistry and to evaluate the technical feasibility of using a neural network to score 3D cavity preparations. Methods: Twenty mandibular molars (tooth 46), each with two carious lesions, were prepared and scored by two expert examiners using a 20-point rubric. The teeth were scanned with a Medit i700® and exported as.OBJ files. Using Open3D, the models were processed into point clouds. With ChatGPT’s guidance, the clinician built a PointNet-based neural model in PyTorch, training it on 20 cases and testing it on 10 unseen preparations. Results: In training, the model achieved an MAE of 0.82, RMSE of 1.02, and Pearson’s r = 0.88, with 66.7% and 93.3% of the predictions within ±5% and ±10% of the examiner scores, respectively. On the test set, it achieved an MAE of 0.97, RMSE of 1.16, and r = 0.92, with 50% and 100% of scores within ±5% and ±10%, respectively. These results show a strong alignment with examiner scores and an early generalizability for scoring preclinical cavity preparations. Conclusions: This study confirms the feasibility of clinician-led, low-cost AI development for 3D cavity assessment using ChatGPT, even without prior coding expertise.
Keywords
3D dental cavity, artificial intelligence, ChatGPT, clinician led AI, dental education, neural networks
Document Type
Journal Article
Date of Publication
11-1-2025
Volume
13
Issue
11
Publication Title
Dentistry Journal
Publisher
MDPI
School
School of Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
El-Hakim, M., Khaled, H., Fawzy, A., & Anthonappa, R. (2025). Clinician-Led development and feasibility of a neural network for assessing 3D dental cavity preparations assisted by conversational AI. Dentistry Journal, 13(11), 531. https://doi.org/10.3390/dj13110531