Evidence map›Paper›PMID 39619711›Full record

ArticleThe Saudi dental journal2024

Assessing the quality of AI information from ChatGPT regarding oral surgery, preventive dentistry, and oral cancer: An exploration study.

Arwa A Alsayed, Mariam B Aldajani, Marwan H Aljohani, Hamdan Alamri, Maram A Alwadi, Bodor Z Alshammari, Falah R Alshammari

Abstract read
In one paragraph

Article in The Saudi dental journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Can Artificial Intelligence Language Models Effectively Address Dental Trauma Questions?Dental traumatology : official publication of International Association for Dental Traumatology · 2025
    Observational
  8. Article
  9. Review
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

7 authors.

Arwa A AlsayedSijam Dental Centre, Riyadh, Saudi Arabia.
Mariam B AldajaniDepartment of Paediatric Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
Marwan H AljohaniOral and Maxillofacial Diagnostic Sciences, College of Dentistry, Taibah University, Madinah city, Saudi Arabia.
Hamdan AlamriDepartment of Preventive Dentistry, College of Dentistry, Majmaah University, Al Majmaah, Saudi Arabia.
Maram A AlwadiDepartment of Dental Health, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.
Bodor Z AlshammariMinistry of Health, Qassim, Saudi Arabia.
Falah R AlshammariCollege of Dentistry, University of Ha'il, Ha'il city, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: Evaluation of the quality of dental information produced by the ChatGPT artificial intelligence language model within the context of oral surgery, preventive dentistry, and oral cancer. Methodology: This study adopted quantitative methods approach. The experts prepared 50 questions (including dimensions of, risk factors, preventive measures, diagnostic methods, and treatment options) that would be presented to ChatGPT, and its responses were rated for their accuracy, completeness, relevance, clarity or comprehensibility, and possible risks using a standardized rubric. To carry out the assessment of the responses by ChatGPT, a standardized scoring rubric was used. Evaluation process included feedback concerning the strengths, weaknesses, and potential areas of improvement in the responses provided by ChatGPT. Results: While achieving the highest score for preventive dentistry at 4.3/5 and being able to communicate the complex information coherently, the tool showed lower accuracy for oral surgery and oral cancer, scoring 3.9/5 and 3.6/5, respectively, with several gaps for post-operative instructions, personalized risk assessments, and specialized diagnostic methods. Potential risks, such as a lack of individualized advice, were shown in 53% of the oral cancer and in 40% of the oral surgery. While showing promise in some domains, ChatGPT had important limitations in specialized areas that require nuanced expertise. Conclusion: The findings point to the need for professional supervision while using AI-generated information and ongoing evaluation as capabilities evolve, for the assurance of responsible implementation in the best interest of patient care.

Indexed as

ChatGPTOral cancerOral surgeryPreventive dentistry

Identifiers

PMID39619711
PMCPMC11605724

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.