Evidence map›Paper›PMID 33384840›Full record

ReviewJournal of dental sciences2021

Developments, application, and performance of artificial intelligence in dentistry - A systematic review.

Sanjeev B Khanagar, Ali Al-Ehaideb, Prabhadevi C Maganur, Satish Vishwanathaiah, Shankargouda Patil, Hosam A Baeshen, Sachin C Sarode, Shilpa Bhandi

2 registry-linked trialsOpen access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of dental sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 285 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
285citing papers in PubMed, 10 pooled it
56.5field-weighted citation impact, top 1% of its field
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.

NCT06325514 completednot on this mapstarted 2024, after this paper: background citation

The Application of an Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis

TypeobservationalSponsorCairo UniversityRan2024 to 2024Enrolled241ConditionsOral Cancer, Oral Lichen Planus, Fordyce Granule, LeukoplakiaArmsArtificial intelligence based program
NCT07027189 nanot yet recruitingnot on this mapstarted 2025, after this paper: background citation

DECIDE-AI:The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection: A Randomized Controlled Study

TypeinterventionalSponsorRadboud University Medical CenterRan2025 to 2026Enrolled25ConditionsArtificial Intelligence (AI) in Diagnosis, Artificial Intelligence Supported Image ReviewingArmsArtificial intelligence in diagnosis
3 · Its place in the literature

Who cites it

285 citing papers in PubMed, 10 syntheses or guidelines pooled it, 585 citations in OpenAlex.

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225 more citing papers are in PubMed but not listed here.

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

8 authors at 4 institutions in 2 countries.

Sanjeev B KhanagarPreventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Ali Al-EhaidebPreventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Prabhadevi C MaganurDepartment of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan, Saudi Arabia.
Satish VishwanathaiahDepartment of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan, Saudi Arabia.
Shankargouda PatilDepartment of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan, Saudi Arabia.
Hosam A BaeshenConsultant in Orthodontics, Department of Orthodontics, College of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
Sachin C SarodeDepartment of Oral and Maxillofacial Pathology, Dr. D.Y.Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411018, Maharashtra, India.
Shilpa BhandiDepartment of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Saudi Arabia.
Jazan University · SAKing Saud bin Abdulaziz University for Health Sciences · SADr. D.Y. Patil Vidyapeeth, Pune · INKing Abdulaziz University · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background/purposeArtificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment. MATERIALS AND

methodsThe literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000-March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2.

resultsAI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination.

conclusionThese studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.

Indexed as

Artificial intelligence dentistryArtificial neural networksComputer-aided diagnosisConvolutional neural networksDeep learning modelsMachine learning

Identifiers

PMID33384840
PMCPMC7770297
OpenAlexW3040455124

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.