ReviewCaries research2026
Assessing the Role of Artificial Intelligence in Caries Detection and Clinical Decision-Making: A Scoping Review.
Review in Caries research, 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) support is expected to increase accuracy and improve treatment plans in dentistry. Nevertheless, AI's ability to promote better oral healthcare is underexplored. This scoping review explores the influence of AI in supporting dental professionals with caries detection and decision-making regarding interventions. SUMMARY: Primary articles indexed on PubMed, Web of Science, Embase, Scopus, and Cochrane Library were searched until August 2025. Studies reporting the differences between participants' caries diagnosis process and decision-making with and without AI were included. Studies only reporting algorithm accuracy, in vitro studies, or studies without an outcome related to caries detection with AI support were excluded. No time and language limits were imposed. Outcomes regarding the influence of AI on the diagnostics process and decision-making were retrieved and narratively summarised. KEY MESSAGES: Thirteen publications were included. Number of participants ranged from 3 to 74, comprising dentists with varying experience and expertise and dental students. Results showed that AI enhances sensitivity, though its impact on specificity varies (10 studies). AI can promote unnecessary interventions for early-stage caries lesions (1 study). AI increased assessment time in two out of three cases (3 studies). AI's cost-effectiveness is uncertain, as greater sensitivity did not lead to better economic outcomes (1 study). In conclusion, AI has the potential to improve diagnostics and influence treatment choices, but current evidence is limited and inconsistent regarding its impact on specificity, decision quality, and cost-effectiveness. Longitudinal studies in clinical settings with long-term follow-ups are needed to understand AI's impact on decision-making.
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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.