Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application-A randomized clinical trial.

Journal: European journal of dental education : official journal of the Association for Dental Education in Europe
PMID: 39082447

Abstract

INTRODUCTION: Radiographic diagnostic competences are a primary focus of dental education. This study assessed two feedback methods to enhance learning outcomes and explored the feasibility of artificial intelligence (AI) to support education.

Authors

  • Sarah Rampf
    Department of Conservative Dentistry, University Hospital Heidelberg, Heidelberg University, Heidelberg, Germany.
  • Holger Gehrig
    Department of Conservative Dentistry, University Hospital Heidelberg, Heidelberg University, Heidelberg, Germany.
  • Andreas Möltner
    Deans Office of the Medical Faculty, Heidelberg University, Heidelberg, Germany.
  • Martin R Fischer
    LMU University Hospital, LMU Munich, Institute for Medical Education, Munich, Germany.
  • Falk Schwendicke
    Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. falk.schwendicke@charite.de.
  • Karin C Huth
    Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Munich, Germany.

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