Evidence map›Paper›PMID 40332142›Full record

ArticleOrthodontics & craniofacial research2025

Performance of AI-Chatbots to Common Temporomandibular Joint Disorders (TMDs) Patient Queries: Accuracy, Completeness, Reliability and Readability.

Mohamed G Hassan, Ahmed A Abdelaziz, Hams H Abdelrahman, Mostafa M Y Mohamed, Mohamed T Ellabban

Abstract read
In one paragraph

Article in Orthodontics & craniofacial research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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.

Mohamed G HassanDivision of Bone and Mineral Diseases, Department of Medicine, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA.ORCID https://orcid.org/0000-0001-9208-3381
Ahmed A AbdelazizDepartment of Orthodontics, Faculty of Dentistry, Assiut University, Assiut, Egypt.
Hams H AbdelrahmanDepartment of Pediatric Dentistry and Dental Public Health, Faculty of Dentistry, Alexandria University, Alexandria, Egypt.
Mostafa M Y MohamedDepartment of Oral Radiology, Faculty of Dentistry, Zarqa University, Zarqa, Jordan.
Mohamed T EllabbanDepartment of Orthodontics, Faculty of Dentistry, Assiut University, Assiut, Egypt.

Funding

Skeletal Disorders Training ProgramT32AR060719 · NIAMS · WASHINGTON UNIVERSITY · PI Roberto Civitelli · 2011 to 2026
$4.6M
NIAMS NIH HHS T32 AR060719
6 · The paper itself

Abstract

TMDs are a common group of conditions affecting the temporomandibular joint (TMJ) often resulting from factors like injury, stress or teeth grinding. This study aimed to evaluate the accuracy, completeness, reliability and readability of the responses generated by ChatGPT-3.5, -4o and Google Gemini to TMD-related inquiries. Forty-five questions covering various aspects of TMDs were created by two experts and submitted by one author to ChatGPT-3.5, ChatGPT-4 and Google Gemini on the same day. The responses were evaluated for accuracy, completeness and reliability using modified Likert scales. Readability was analysed with six validated indices via a specialised tool. Additional features, such as the inclusion of graphical elements, references and safeguard mechanisms, were also documented and analysed. The Pearson Chi-Square and One-Way ANOVA tests were used for data analysis. Google Gemini achieved the highest accuracy, providing 100% correct responses, followed by ChatGPT-3.5 (95.6%) and ChatGPT-4o (93.3%). ChatGPT-4o provided the most complete responses (91.1%), followed by ChatGPT-03 (64.4%) and Google Gemini (42.2%). The majority of responses were reliable, with ChatGPT-4o at 93.3% 'Absolutely Reliable', compared to 46.7% for ChatGPT-3.5 and 48.9% for Google Gemini. Both ChatGPT-4o and Google Gemini included references in responses, 22.2% and 13.3%, respectively, while ChatGPT-3.5 included none. Google Gemini was the only model that included multimedia (6.7%). Readability scores were highest for ChatGPT-3.5, suggesting its responses were more complex than those of Google Gemini and ChatGPT-4o. Both ChatGPT-4o and Google Gemini demonstrated accuracy and reliability in addressing TMD-related questions, with their responses being clear, easy to understand and complemented by safeguard statements encouraging specialist consultation. However, both platforms lacked evidence-based references. Only Google Gemini incorporated multimedia elements into its answers.

Indexed as

ComprehensionTemporomandibular Joint DisordersGenerative Artificial IntelligenceHumansInternetReproducibility of ResultsSurveys and Questionnairesartificial intelligencechatbotChatGPTeducationGoogle Geminitemporomandibular jointtemporomandibular joint disorders

Identifiers

PMID40332142
PMCPMC13228120

What OpenQuestion holds

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LicenceTDM
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.