Evidence map›Paper›PMID 39398765›Full record

ReviewCureus2024

AI-Driven Innovations in Pediatric Dentistry: Enhancing Care and Improving Outcome.

Nawaf Alharbi, Adel S Alharbi

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Review
  9. Article
  10. Review
  11. Article
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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

2 authors.

Nawaf AlharbiMedicine, Riyadh Elm University, Riyadh, SAU.
Adel S AlharbiPediatrics, Prince Sultan Military Medical City, Ministry of Defense, Riyadh, SAU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming pediatric dentistry by enhancing diagnostic accuracy, streamlining treatment planning, and improving behavior management. This review explores current AI applications in detecting dental anomalies, categorizing fissure sealants, assessing chronological age, and managing patient behavior. The review also identifies emerging trends and future directions in AI technology that promise to further revolutionize pediatric dental care. By synthesizing recent research and clinical studies, this review aimed to inform dental professionals and researchers about the potential of AI to address traditional challenges and improve oral health outcomes for children.

Indexed as

artificial intelligencebehavior managementneural networkoral health outcomespediatric dentistry

Identifiers

PMID39398765
PMCPMC11470390

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.