Artificial intelligence in dental education: ChatGPT's performance on the periodontic in‐service examination
Bibliographic record
Abstract
BACKGROUND: ChatGPT's (Chat Generative Pre-Trained Transformer) remarkable capacity to generate human-like output makes it an appealing learning tool for healthcare students worldwide. Nevertheless, the chatbot's responses may be subject to inaccuracies, putting forth an intense risk of misinformation. ChatGPT's capabilities should be examined in every corner of healthcare education, including dentistry and its specialties, to understand the potential of misinformation associated with the chatbot's use as a learning tool. Our investigation aims to explore ChatGPT's foundation of knowledge in the field of periodontology by evaluating the chatbot's performance on questions obtained from an in-service examination administered by the American Academy of Periodontology (AAP). METHODS: ChatGPT3.5 and ChatGPT4 were evaluated on 311 multiple-choice questions obtained from the 2023 in-service examination administered by the AAP. The dataset of in-service examination questions was accessed through Nova Southeastern University's Department of Periodontology. Our study excluded questions containing an image as ChatGPT does not accept image inputs. RESULTS: test. A p value below the threshold of 0.05 was deemed statistically significant. CONCLUSION: While ChatGPT4 showed a higher proficiency compared to ChatGPT3.5, both chatbot models leave considerable room for misinformation with their responses relating to periodontology. The findings of the study encourage residents to scrutinize the periodontic information generated by ChatGPT to account for the chatbot's current limitations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".