Evidence map›Paper›PMID 41559655›Full record

ArticleBMC oral health2026

Knowledge and awareness of dental residents regarding the applications of artificial intelligence for diagnosis and treatment planning- a cross-sectional survey.

Faiza Ali, Fizzah Ikram, Zahra Masood, Dinaz Ghandhi, Rashna Hoshang Sukhia

Abstract read
In one paragraph

Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Faiza AliDepartment of Dentistry and Oral Health Sciences, The Aga Khan University Hospital, Stadium Road, P.O Box 3500, Karachi, 74800, Pakistan.
Fizzah IkramDepartment of Dentistry and Oral Health Sciences, The Aga Khan University Hospital, Stadium Road, P.O Box 3500, Karachi, 74800, Pakistan.
Zahra MasoodDepartment of Dentistry and Oral Health Sciences, The Aga Khan University Hospital, Stadium Road, P.O Box 3500, Karachi, 74800, Pakistan.
Dinaz GhandhiAltamash Institute of Dental Medicine, 2-R Sunset Boulevard, DHA Phase 4, Karachi, Pakistan.
Rashna Hoshang SukhiaDepartment of Dentistry and Oral Health Sciences, The Aga Khan University Hospital, Stadium Road, P.O Box 3500, Karachi, 74800, Pakistan. rashna.aga@aku.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the high pace of development in technology worldwide, Artificial intelligence (AI) has given a new horizon to the world of medicine and dentistry. Numerous research studies have been carried out over the past decade that have assessed the role of Machine learning (ML) and artificial intelligence in dentistry. This study aims to evaluate dental residents' knowledge and perception regarding AI's application in dentistry.

methodsA validated and reliable questionnaire was circulated online among 32 dental residents. The small sample size reflects the total number of dental residents available at the study site during the data-collection period. The questionnaire consisted of 20 components, which assessed knowledge and awareness of participants regarding artificial intelligence (AI) and its uses in dentistry. The Shapiro-Wilk test was applied to assess the normality of the data. For each categorical variable, frequencies and percentages were computed. For quantitative variables such as age, the arithmetic mean and standard deviation were computed.

resultsOut of the 32 participants, 26 were female residents, and the remaining six were male residents. The majority of participants had no prior experience working on AI-related projects, and 97% had never received any training on AI applications. About 31% claimed to have a basic understanding of AI's working principles. Regarding the perceived diagnostic abilities of AI compared to humans, 15% believed AI was superior, while 65% were unsure. A significant 71% believed AI could assist in identifying minute details on radiographs often overlooked by human practitioners. Moreover, 72% expressed the possibility of using AI for diagnosis in the future, and 31% viewed AI as most helpful in making a diagnosis.

conclusionsThe majority of clinicians are aware of artificial intelligence and its significance. Clinicians harbor doubt regarding the diagnostic abilities of artificial intelligence and, hence, prefer their judgment. Despite the lack of confidence, the majority believes in AI´s ability to pinpoint minor details which helps in the accurate diagnosis of dental pathologies. Basic AI skills would help clinicians learn and utilize technology to their advantage.

Indexed as

Artificial IntelligenceHealth Knowledge, Attitudes, PracticeInternship and ResidencyPatient Care PlanningAdultCross-Sectional StudiesFemaleHumansMaleSurveys and QuestionnairesArtificial intelligenceDeep learningDental residentsDiagnosis and treatment planningMachine learning

Identifiers

PMID41559655
PMCPMC12930827

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.