ArticleCaries research2026
Human-AI Collaboration in Proximal Early Caries Lesion Detection: Potential Benefits for Novices and Potential Risks for Experts.
Article 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEarly-stage proximal caries detection on bitewing radiographs is challenging and hampered by inter-observer variability. This study aimed to assess how artificial intelligence (AI) support influences radiographic detection and staging of proximal early caries lesions, confidence, and assessment time across observer experience levels.
methodsEighteen observers (dental students, general dentists, and dental radiologists) evaluated 369 proximal surfaces on 25 bitewing radiographs in two sessions: first unaided, then aided with AI-annotated caries lesion regions. Observers assigned lesion stages and provided self-reported (SR) confidence ratings. Radiographic detection and staging accuracy relative to an expert consensus reference standard was assessed using cumulative link mixed models. Ordinal agreement and binary detection metrics were computed for each observer group.
resultsThe AI-based tool had divergent effects across the different groups of observers. Dental students showed a statistically significant improvement in radiographic detection and staging accuracy relative to the reference standard (p = 0.039). General dentists and dental radiologists showed no statistically significant change in radiographic detection and staging accuracy when using AI support (p = 0.28 and p = 0.15, respectively). SR confidence increased when the AI-based tool was used for all groups.
conclusionAI support induced a shift in observers' detections toward the AI-based tool's performance pattern, with its effect on radiographic detection and staging accuracy varying across observer groups and no statistically significant change observed in more experienced observers. AI generally increased the SR confidence of the clinicians.
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.