ReviewPublic health challenges2026
AI-Driven Dentistry and Public Health Surveillance: Opportunities and Challenges.
Review in Public health challenges, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into dentistry is reshaping clinical workflows, opening new possibilities for population-level public health monitoring. Machine learning approaches, such as convolutional neural networks and other deep learning architectures, are becoming more and more capable to exhibit diagnostic performances, which are close to those of expert human readers. Beyond single-clinic decision support, aggregated outputs that have been collected from AI diagnostic systems could be used as real-time signals for population surveillance: When combined with geospatial and socioeconomic datasets, they may help reveal structural barriers to care. This review presents opportunities and constraints at the intersection of diagnostic AI and dental public health surveillance and outlines a five-stage framework (data ingestion, spatiotemporal aggregation, socioeconomic enrichment, predictive modeling, and dashboard deployment) for turning de-identified AI outputs into actionable, equity-focused public health intelligence. This article also examines methodological, privacy, and governance challenges-including bias, interpretability, and accountability-as well as ethical issues and proposes safeguards to support equitable deployment.
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What Socratic 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 Socratic graph.