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Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial
Sarah Rampf1, Holger Gehrig1, Andreas Möltner2
1Department of Conservative Dentistry, University Hospital Heidelberg, Heidelberg University, Heidelberg, Germany.
Summary
Elaborated feedback significantly improved dental students' radiographic diagnostic skills in detecting enamel caries and apical periodontitis. Artificial intelligence (AI) demonstrated high accuracy, suggesting its potential to aid dental education.
Area of Science:
- Dental Education
- Radiology
- Artificial Intelligence
Background:
- Radiographic diagnostic competence is crucial in dental education.
- This study evaluated feedback methods and AI feasibility for enhancing dental radiography learning outcomes.
Purpose of the Study:
- To assess the impact of elaborated feedback versus knowledge of results feedback on dental students' diagnostic competences.
- To explore the feasibility of using artificial intelligence (AI) to support dental radiographic education.
Main Methods:
- Fourth-year dental students used virtual radiological cases with either elaborated feedback (eF) or knowledge of results feedback (KOR).
- Diagnostic competences were tested on radiographs for caries, apical periodontitis, and image quality.
- The diagnostic accuracy of an AI system (dentalXrai Pro 3.0) was also assessed.
Main Results:
- The elaborated feedback group showed significantly better detection of enamel caries and apical periodontitis compared to the KOR group.
- Elaborated feedback also improved assessment of periapical image quality.
- The AI system exhibited near-perfect diagnostic performance across various metrics.
Conclusions:
- Elaborated feedback enhances dental students' radiographic diagnostic skills, especially for caries and apical periodontitis.
- Artificial intelligence shows promise as a tool to assist in radiographic interpretation and education, potentially serving as an alternative to expert labeling.
