ReviewFrontiers in dental medicine2025
The role of artificial intelligence in diagnosing pediatric dental disorders-a narrative review.
Review in Frontiers in dental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled 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.
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.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence in periapical radiography: a systematic review.Odontology · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is revolutionizing healthcare, and its application in pediatric dentistry is showing significant promise in improving diagnostic accuracy, efficiency, and early detection of dental conditions in children. This review explores the current landscape of AI-driven technologies employed in the identification of pediatric dental diseases, including dental caries, malocclusion, developmental anomalies, and periodontal conditions. Various AI techniques, such as machine learning, deep learning, and convolutional neural networks (CNNs), are examined for their diagnostic potential and performance relative to traditional methods. The review also examines the integration of AI with radiographic imaging, intraoral scanners, and other diagnostic tools commonly used in pediatric dental practice. While AI presents considerable advantages such as speed, objectivity, and the potential to reduce human error, limitations, including data privacy, lack of standardized datasets, and ethical considerations, are also highlighted. Overall, this review underscores the ground-breaking potential of AI in pediatric dentistry and emphasizes the need for further research, validation, and clinical integration to realize its benefits fully.
Indexed as
Identifiers
What OpenQuestion holds
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.