Evidence map›Paper›PMID 40978608›Full record

ArticleBioinformation2025

Survey on awareness of artificial intelligence in pediatric dentistry.

Shilpa S Naik, Jasmin Winner, Ishani Ratnaparkhi, Shirin Chavan, Mayuri Gorule, Ruchi Chaudhary

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

6 authors.

Shilpa S NaikDepartment of Pediatric Dentistry, D.Y. Patil Deemed to be University, School of Dentistry, Navi Mumbai, Maharashtra, India.
Jasmin WinnerDepartment of Pediatric Dentistry, D.Y. Patil Deemed to be University, School of Dentistry, Navi Mumbai, Maharashtra, India.
Ishani RatnaparkhiDepartment of Pediatric Dentistry, D.Y. Patil Deemed to be University, School of Dentistry, Navi Mumbai, Maharashtra, India.
Shirin ChavanDepartment of Pediatric Dentistry, D.Y. Patil Deemed to be University, School of Dentistry, Navi Mumbai, Maharashtra, India.
Mayuri GoruleDepartment of Pediatric Dentistry, D.Y. Patil Deemed to be University, School of Dentistry, Navi Mumbai, Maharashtra, India.
Ruchi ChaudharyDepartment of Pediatric Dentistry, D.Y. Patil Deemed to be University, School of Dentistry, Navi Mumbai, Maharashtra, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming pediatric dentistry by improving diagnostic accuracy, treatment planning and patient care. Therefore, it is of interest to assess the knowledge, awareness and attitudes of pediatric dental professionals in India toward AI integration. While the majority recognized the benefits of AI, significant knowledge gaps and barriers such as cost, ethical concerns and lack of training were identified. A statistically significant association was found between awareness levels and years of experience as well as practice settings. These findings underscore the need for structured educational programs to support AI adoption in pediatric dentistry.

Indexed as

Artificial intelligencediagnostic accuracypediatric dentistrytreatment planning

Identifiers

PMID40978608
PMCPMC12449532

What OpenQuestion holds

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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.