Evidence map›Paper›PMID 39917699›Full record

ReviewFrontiers in dental medicine2024

Artificial intelligence in dentistry and dental biomaterials.

Dinesh Rokaya, Ahmad Al Jaghsi, Rohan Jagtap, Viritpon Srimaneepong

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in dental medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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  18. Supernumerary molars' prevalence in a Romanian population sample and their potential value in forensic investigation.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
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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

4 authors.

Dinesh RokayaClinical Sciences Department, College of Dentistry, Ajman University, Ajman, United Arab Emirates.
Ahmad Al JaghsiClinical Sciences Department, College of Dentistry, Ajman University, Ajman, United Arab Emirates.
Rohan JagtapDivision of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center (UMMC) School of Dentistry, Jackson, MS, United States.
Viritpon SrimaneepongDepartment of Prosthodontics, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) technology is being used in various fields and its use is increasingly expanding in dentistry. The key aspects of AI include machine learning (ML), deep learning (DL), and neural networks (NNs). The aim of this review is to present an overview of AI, its various aspects, and its application in biomedicine, dentistry, and dental biomaterials focusing on restorative dentistry and prosthodontics. AI-based systems can be a complementary tool in diagnosis and treatment planning, result prediction, and patient-centered care. AI software can be used to detect restorations, prosthetic crowns, periodontal bone loss, and root canal segmentation from the periapical radiographs. The integration of AI, digital imaging, and 3D printing can provide more precise, durable, and patient-oriented outcomes. AI can be also used for the automatic segmentation of panoramic radiographs showing normal anatomy of the oral and maxillofacial area. Recent advancement in AI in medical and dental sciences includes multimodal deep learning fusion, speech data detection, and neuromorphic computing. Hence, AI has helped dentists in diagnosis, planning, and aid in providing high-quality dental treatments in less time.

Indexed as

artificial intelligencedeep learningdental biomaterialsdental medicinedentistrymachine learningmedicineneural networks

Identifiers

PMID39917699
PMCPMC11797767

What OpenQuestion holds

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LicenceCC BY
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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.