SynthesisInternational dental journal2026
Explainable Artificial Intelligence in Dentistry: A Systematic Review of Its Trust and Translation.
Synthesis 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 2 papers, 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable artificial intelligence in dental imaging: a systematic review of interpretability and the current state of trust evidence.Frontiers in dental medicine · 2026Pooled it
- Article
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
INTRODUCTION AND
aimsExplainable artificial intelligence (XAI) is a set of methods and processes that make the decisions of artificial intelligence (AI) models understandable to those who are not conversant with the technology. This "black box" nature of complex AI models appears to be a primary barrier to their clinical adoption in health sciences, including dentistry. XAI is being touted as a solution to build clinician trust. This review critically assesses whether current dental XAI research is methodologically rigorous enough to substantiate claims of enhanced trustworthiness.
methodsThis systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, searching PubMed, IEEE Xplore, medRxiv, and Ovid for dental XAI studies (2015-2025). We assessed the risk of bias and applicability using QUADAS-2 and PROBAST.
resultsNineteen of the 100 identified studies met the inclusion criteria. Although these studies used diverse XAI techniques, including image-based saliency methods (eg, Grad-CAM), feature attribution approaches (eg, SHAP), and local approximation methods (eg, LIME) across various dental specialties, quality assessment exposed significant limitations. Most (14 of 19) exhibited a high risk of bias, driven by small retrospective datasets, lack of external validation, and weak reference standards. Interestingly, only 1 study has evaluated the impact of XAI on human understanding.
conclusionCurrent dental XAI research remains in a proof-of-concept phase, characterised by technical demonstrations based on low-quality evidence. The field has not yet substantiated claims that XAI enhances clinical trust or decision-making. To bridge this gap, future work must prioritise methodological rigour, external validation, and, most importantly, human-centred evaluations with dental professionals to measure the true impact of explainability on clinical workflows and patient care. CLINICAL RELEVANCE: Current dental XAI lacks the evidence quality required for clinical reliance. Practitioners should exercise caution, as these tools have not been proven to actually improve diagnostic accuracy or trust in daily practice. Until validated in real-world settings, XAI remains experimental technology rather than a standard of care.
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Identifiers
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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.