Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Dentistry Students vs. ChatGPT 4o: Assessing Knowledge of Panoramic Radiographic Anatomy
1
Zitationen
2
Autoren
2015
Jahr
Abstract
Objectives: Panoramic radiography constitutes an essential modality for the comprehensive assessment of patients in dentistry.This study investigates the panoramic radiography knowledge between dentistry students and ChatGPT-4o. Materials and Methods:In this study, 182 undergraduate students from Kocaeli Health and Technology University, Faculty of Dentistry participated, with 52.7% being 3rd grade students and 47.3% being 4th grade students.Participants identified 35 landmarks on panoramic radiographs, which were categorized into bone structures, soft tissues/airways, and ghost images (20, 13, 2 questions were asked respectively).ChatGPT-4o was evaluated on the same task using the identical set of panoramic radiographic anatomy landmarks.To ensure consistency, the questions were entered into the AI model in the same format as they were presented to the students.ChatGPT-4o's responses were recorded for further evaluation.All data were analyzed using IBM SPSS version 23. Results:The study revealed that 4th-grade students demonstrated significantly higher correct answer rates in bone structures, ghost images, and total scores compared to 3rd-grade students.No significant differences were observed in the median values of correct answer rates according to age and gender. Conclusions:The outcome that 4th-grade students exhibited greater competence compared to 3rd-graders suggests that practical exposure to panoramic radiography within clinical contexts may enhance knowledge retention despite comprehensive theoretical instruction.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.460 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.341 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.791 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.536 Zit.