ChatGPT’s performance in dentistry and allergyimmunology assessments: a comparative study


  • Alexander Fuchs Department of Periodontology, Endodontology, and Cariology, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
  • Tina Trachsel Division of Allergy, University Children's Hospital Basel, Basel, Switzerland
  • Roland Weiger Department of Periodontology, Endodontology, and Cariology, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
  • Florin Eggmann Department of Periodontology, Endodontology, and Cariology, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland





Allergology, Artificial intelligence, Dental education, Clinical immunology, Machine learning, Medical informatics applications


Large language models (LLMs) such as ChatGPT have potential applications in healthcare, including dentistry. Priming, the practice of providing LLMs with initial, relevant information, is an approach to improve their output quality. This study aimed to evaluate the performance of ChatGPT 3 and ChatGPT 4 on self-assessment questions for dentistry, through the Swiss Federal Licensing Examination in Dental Medicine (SFLEDM), and allergy and clinical immunology, through the European Examination in Allergy and Clinical Immunology (EEAACI). The second objective was to assess the impact of priming on ChatGPT’s performance. The SFLEDM and EEAACI multiple-choice questions from the University of Bern’s Institute for Medical Education platform were administered to both ChatGPT versions, with and without priming. Performance was analyzed based on correct responses. The statistical analysis included Wilcoxon rank sum tests (α=0.05). The average accuracy rates in the SFLEDM and EEAACI assessments were 63.3% and 79.3%, respectively. Both ChatGPT versions performed better on EEAACI than SFLEDM, with ChatGPT 4 outperforming ChatGPT 3 across all tests. ChatGPT 3's performance exhibited a significant improvement with priming for both EEAACI (p=0.017) and SFLEDM (p=0.024) assessments. For ChatGPT 4, the priming effect was significant only in the SFLEDM assessment (p=0.038). The performance disparity between SFLEDM and EEAACI assessments underscores ChatGPT’s varying proficiency across different medical domains, likely tied to the nature and amount of training data available in each field. Priming can be a tool for enhancing output, especially in earlier LLMs. Advancements from ChatGPT 3 to 4 highlight the rapid developments in LLM technology. Yet, their use in critical fields such as healthcare must remain cautious owing to LLMs’ inherent limitations and risks.


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How to Cite

Fuchs, A., Trachsel, T., Weiger, R., & Eggmann, F. (2024). ChatGPT’s performance in dentistry and allergyimmunology assessments: a comparative study. SWISS DENTAL JOURNAL SSO – Science and Clinical Topics, 134(2), 1–17.