ArticleInternational dental journal2026
Knowledge and Awareness of Performance Metrics in Dental Image Analysis With Artificial Intelligence: A Cross-Sectional Survey of Researchers and Clinicians.
Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Knowledge and Awareness of Performance Metrics in Dental Image Analysis With Artificial Intelligence: A Cross-Sectional Survey of Researchers and Clinicians.International dental journal · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
aimAppropriate performance metrics are essential for evaluating machine learning (ML) algorithm performance in dental image analysis. A broad variety of performance metrics are used in dental artificial intelligence (AI) research which could cause confusion regarding model performance. This study aimed to assess the knowledge, awareness, and self-reported confidence of oral health researchers and clinicians regarding AI performance metrics.
methodsThis cross-sectional, questionnaire-based survey initially recruited participants (n = 100) from the ITU/WHO/WIPO Global Initiative on AI for Health Topic Group - Oral Health and affiliated professional global networks. An online self-administered questionnaire evaluated theoretical knowledge and applied knowledge. Participants also reported basic demographics and their confidence in using different metrics.
resultsOverall performance was poor with participants achieving a mean score of 17.1% (SD: 15.2%, median: 16.7%, IQR: 16.7%) for fully correct responses. Responses to theoretical questions were rarely fully correct (range: 1%-15%; median: 5.5%). Applied scenario performance ranged from 17% to 37% correct responses (median: 29%). Participants demonstrated greater knowledge of common metrics (accuracy, specificity) but struggled with advanced measures (Intersection over Union & Dice Similarity Coefficient). Common errors included terminology confusion, familiarity bias towards accuracy, and task-metric misalignment.
conclusionSubstantial gaps exist in knowledge and awareness of both theory and practical applications of AI performance metrics amongst dental professionals and researchers. Frequent misapplication of commonly known metrics suggests overgeneralisation without consideration of task-specific requirements. CLINICAL RELEVANCE: Inadequate knowledge or awareness of metric selection and interpretation may lead to the deployment of suboptimal algorithms, which can compromise diagnostic accuracy and patient outcomes. Enhanced understanding of performance metrics is critical for ensuring reliable, fair, safe, and effective AI systems in clinical dentistry.
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Registered trials
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