Evidence map›Paper›PMID 42013516›Full record

ArticleInternational dental journal2026

AI Chatbots vs. Traditional Sources: Dental Health Literacy and Confidence Among Dental Patients-A Cross-Sectional Study.

Sachin Naik, Sajith Vellappally, Mohammed Alateek, Yasser Fahad Alrayyes, Abdul Aziz Abdullah Al Kheraif, Talal Mughaileth Alnassar, Gerhard Schmalz, Ziyad Mohammed Alsultan, Haya Alayadi, Nandita Suresh and 2 more

Abstract readComparative Study
In one paragraph

Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Sachin NaikDental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia. Electronic address: snaik@ksu.edu.sa.
Sajith VellappallyDental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.
Mohammed AlateekDental University Hospital, King Saud University, Riyadh, Saudi Arabia.
Yasser Fahad AlrayyesDental University Hospital, King Saud University, Riyadh, Saudi Arabia.
Abdul Aziz Abdullah Al KheraifDental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.
Talal Mughaileth AlnassarProsthetic Dental Science Department, College of Dentistry, King Saud University, Riyadh, Saudi Arabia.
Gerhard SchmalzDepartment of Conservative Dentistry and Periodontology, Brandenburg Medical School Theodor Fontane (MHB), Brandenburg an der Havel, Germany.
Ziyad Mohammed AlsultanDental University Hospital, King Saud University, Riyadh, Saudi Arabia.
Haya AlayadiDental Health Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.
Nandita SureshDepartment of Oral and Maxillofacial Diseases, Helsinki University and University Hospital, Helsinki, Finland; Global Research Cell, Dr. D. Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pune, India.
Sukumaran AnilGlobal Research Cell, Dr. D. Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pune, India; Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.
Avneesh ChopraDepartment of Conservative Dentistry and Periodontology, Brandenburg Medical School Theodor Fontane (MHB), Brandenburg an der Havel, Germany; International Faculty, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India. Electronic address: avneesh.chopra@mhb-fontane.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesGiven the increasing use of artificial intelligence (AI) tools for dental health information, this study compared patients' perceived health information literacy for AI chatbots, measured using the Artificial Intelligence-eHealth Literacy Scale (AI-eHEALS), with traditional health information literacy, measured using the Traditional Health Information Literacy Scale (THILS) and examined how literacy and confidence influence information source preferences. MATERIALS AND

methodsA cross-sectional study was conducted at King Saud University Dental Hospital in Riyadh, Saudi Arabia, collecting responses from 474 adult dental patients using 8 validated items from AI-eHEALS and THILS. Domain-wise scores were calculated, and assumptions for parametric testing were verified. Group differences were analysed using independent t-tests. Structural equation modelling (SEM) examined pathways linking literacy domains, confidence, and information-source preferences, with perceived knowledge and evaluation items combined into a single AI-eHEALS literacy construct.

resultsThe THILS scores were significantly higher than those on the AI-eHEALS across all domains (p < .001). AI-eHEALS scores were positively associated with prior AI use, male gender, and employment status, while THILS showed few demographic links. SEM indicated that combined literacy (knowledge + evaluation) predicted confidence in using AI tools (β = 0.62). Usefulness items showed weak loadings and were excluded, improving model reliability. Participants reported moderate familiarity with AI chatbots; however, they demonstrated higher scores and greater agreement with traditional sources.

conclusionsThe findings indicate that traditional dental health sources remain preferred over AI chatbots for health information, even with improved digital literacy. The study provides insight into patient perceptions of AI tools in dental care and suggests a need for enhanced AI literacy education and transparent communication about AI capabilities and limitations in health information contexts.

Indexed as

Artificial IntelligenceHealth LiteracyAdultCross-Sectional StudiesDigital HealthFemaleHumansMaleMiddle AgedSaudi ArabiaSurveys and QuestionnairesYoung AdultArtificial intelligenceChatbotsDental healtheHealth literacyHealth informationPatient attitudes

Identifiers

PMID42013516
PMCPMC13121421

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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 Socratic graph.