Evidence map›Paper›PMID 40377826›Full record

ArticleOral radiology2025

Artificial intelligence in dentistry: awareness among dentists and computer scientists.

Eliana Dantas Costa, Marcela Amanda Vieira, Glaucia Maria Bovi Ambrosano, Hugo Gaêta-Araujo, José Andery Carneiro, Breno Augusto Guerra Zancan, Alexander Scaranti, Alessandra Alaniz Macedo, Camila Tirapelli

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Article in Oral radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

9 authors.

Eliana Dantas CostaDepartment of Dental Materials and Prosthodontics, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil. edantasc@yahoo.com.br.ORCID 0000-0003-4463-7436
Marcela Amanda VieiraDepartment of Dental Materials and Prosthodontics, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
Glaucia Maria Bovi AmbrosanoDepartment of Social Dentistry, Division of Biostatistics, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil.
Hugo Gaêta-AraujoDepartment of Stomatology, Public Health, and Forensic Dentistry, Division of Oral Radiology, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
José Andery CarneiroDepartment of Computing and Mathematics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
Breno Augusto Guerra ZancanDepartment of Computing and Mathematics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
Alexander ScarantiDepartment of Computing and Mathematics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
Alessandra Alaniz MacedoDepartment of Computing and Mathematics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
Camila TirapelliDepartment of Dental Materials and Prosthodontics, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.

Funding

Fundação de Amparo à Pesquisa do Estado de São Paulo - FAPESP 2022/02253-2
6 · The paper itself

Abstract

objectivesFor clinical application of artificial intelligence (AI) in dentistry, collaboration with computer scientists is necessary. This study aims to evaluate the knowledge of dentists and computer scientists regarding the utilization of AI in dentistry, especially in dentomaxillofacial radiology.

methods610 participants (374 dentists and 236 computer scientists) took part in a survey about AI in dentistry and radiographic imaging. Response options contained Likert scale of agreement/disagreement. Descriptive analyses of agreement scores were performed using quartiles (minimum value, first quartile, median, third quartile, and maximum value). Non-parametric Mann-Whitney test was used to compare response scores between two categories (α = 5%).

resultsDentists academics had higher agreement scores for the questions: "knowing the applications of AI in dentistry", "dentists taking the lead in AI research", "AI education should be part of teaching", "AI can increase the price of dental services", "AI can lead to errors in radiographic diagnosis", "AI can negatively interfere with the choice of Radiology specialty", "AI can cause a reduction in the employment of radiologists", "patient data can be hacked using AI" (p < 0.05). Computer scientists had higher concordance scores for the questions "having knowledge in AI" and "AI's potential to speed up and improve radiographic diagnosis".

conclusionAlthough dentists acknowledge the potential benefits of AI in dentistry, they remain skeptical about its use and consider it important to integrate the topic of AI into dental education curriculum. On the other hand, computer scientists confirm technical expertise in AI and recognize its potential in dentomaxillofacial radiology.

Indexed as

Artificial IntelligenceDentistryDentistsAdultFemaleHumansMaleMiddle AgedSurveys and QuestionnairesArtificial intelligenceDentistryMedical informaticsQuestionnairesRadiology

Identifiers

PMID40377826

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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 OpenQuestion graph.