Evidence map›Paper›PMID 41040620›Full record

ArticleJournal of dental sciences2025

Performance of artificial intelligence chatbots in National dental licensing examination.

Chad Chan-Chia Lin, Jui-Sheng Sun, Chin-Hao Chang, Yu-Han Chang, Jenny Zwei-Chieng Chang

Abstract read
In one paragraph

Article in Journal of dental sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chad Chan-Chia LinSchool of Dentistry, College of Medicine, National Taiwan University, Taipei, Taiwan.
Jui-Sheng SunDepartment of Orthopedic Surgery, Landseed International Hospital, Taoyuan City, Taiwan.
Chin-Hao ChangDepartment of Medical Research, National Taiwan University Hospital, Taipei, Taiwan.
Yu-Han ChangClinical Trial Center, National Taiwan University Hospital, Taipei, Taiwan.
Jenny Zwei-Chieng ChangSchool of Dentistry, College of Medicine, National Taiwan University, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/purpose: The Taiwan dental board exams comprehensively assess dental candidates across twenty distinct subjects, spanning foundational knowledge to clinical fields, using multiple-choice single-answer exams with a minimum passing score of 60 %. This study assesses the performance of artificial intelligence (AI)-powered chatbots (specifically ChatGPT3.5, Gemini, and Claude2), categorized as Large Language Models (LLMs), on these exams from 2021 to 2023. Materials and methods: A total of 2699 multiple-choice questions spanning eight subjects in basic dentistry and twelve in clinical dentistry were analyzed. Questions involving images and tables were excluded. Statistical analyses were conducted using McNemar's test. Furthermore, annual results of LLMs were compared with the qualification rates of human candidates to provide additional context. Results: Claude2 demonstrated the highest overall accuracy (54.89 %) on the Taiwan national dental licensing examinations, outperforming ChatGPT3.5 (49.33 %) and Gemini (44.63 %), with statistically significant differences in performance across models. In the basic dentistry domain, Claude2 scored 59.73 %, followed by ChatGPT3.5 (54.87 %) and Gemini (47.35 %). Notably, Claude2 excelled in biochemistry (73.81 %) and oral microbiology (88.89 %), while ChatGPT3.5 also performed strongly in oral microbiology (80.56 %). In the clinical dentistry domain, Claude2 led with a score of 52.45 %, surpassing ChatGPT3.5 (46.54 %) and Gemini (43.26 %), and showed strong results in dental public health (65.81 %). Despite these achievements, none of the LLMs attained passing scores overall. Conclusion: None of the models achieved passing scores, highlighting their strengths in foundational knowledge but limitations in clinical reasoning.

Indexed as

Artificial intelligenceChatbotDental educationDentistryLarge language models

Identifiers

PMID41040620
PMCPMC12485390

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.