Evidence map›Paper›PMID 40013098›Full record

ArticleFrontiers in oral health2025

Evaluating ChatGPT-4's performance on oral and maxillofacial queries: Chain of Thought and standard method.

Kaiyuan Ji, Zhihan Wu, Jing Han, Guangtao Zhai, Jiannan Liu

Abstract read
In one paragraph

Article in Frontiers in oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Comparative diagnostic accuracy of ChatGPT models in salivary gland disease: a multimodal vignette-based evaluation.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
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.

Kaiyuan Ji *School of Communication and Electronic Engineering, East China Normal University, Shanghai, China.
Zhihan Wu *Department of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jing HanDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Guangtao ZhaiSchool of Communication and Electronic Engineering, East China Normal University, Shanghai, China.
Jiannan LiuDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Oral and maxillofacial diseases affect approximately 3.5 billion people worldwide. With the continuous advancement of Artificial Intelligence technologies, particularly the application of generative pre-trained transformers like ChatGPT-4, there is potential to enhance public awareness of the prevention and early detection of these diseases. This study evaluated the performance of ChatGPT-4 in addressing oral and maxillofacial disease questions using standard approaches and the Chain of Thought (CoT) method, aiming to gain a deeper understanding of its capabilities, potential, and limitations. Materials and methods: Three experts, drawing from their extensive experience and the most common questions in clinical settings, selected 130 open-ended questions and 1,805 multiple-choice questions from the national dental licensing examination. These questions encompass 12 areas of oral and maxillofacial surgery, including Prosthodontics, Pediatric Dentistry, Maxillofacial Tumors and Salivary Gland Diseases, and maxillofacial Infections. Results: Using CoT approach, ChatGPT-4 exhibited marked enhancements in accuracy, structure, completeness, professionalism, and overall impression for open-ended questions, revealing statistically significant differences compared to its performance on general oral and maxillofacial inquiries. In the realm of multiple-choice questions, the application of CoT method boosted ChatGPT-4's accuracy across all major subjects, achieving an overall accuracy increase of 3.1%. Conclusions: When employing ChatGPT-4 to address questions in oral and maxillofacial surgery, incorporating CoT as a querying method can enhance its performance and help the public improve their understanding and awareness of such issues. However, it is not advisable to consider it a substitute for doctors.

Indexed as

Artificial IntelligenceChain of ThoughtChatGPT-4education tooloral and maxillofacial

Identifiers

PMID40013098
PMCPMC11860867

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.