Explainable artificial intelligence and clinician trust in dentistry: a contested claim

Explainable artificial intelligence is widely promoted as the mechanism by which opaque dental models will earn clinician trust, but the claim has acquired momentum faster than empirical support. This critical narrative review weighs the evidence behind three positions: that explanation is necessary for trust, that explanation does not reliably improve decisions, and that explanation helps only under specific conditions. The evidence converges on the perception that opaque models are a barrier to adoption, but it diverges on whether explanation improves diagnostic accuracy and whether it calibrates or inflates trust. Each divergence is traced to a concrete methodological cause rather than to contextual difference: the studies measure confidence instead of calibrated trust, they compare different explanation formats with small clinician samples, and they are drawn almost entirely from medical rather than dental settings. The argued position is that explanation is valuable in dentistry only when it is faithful, semantically meaningful and evaluated against human decisions, and that the dental field has not yet produced such evidence. Two tables map the positions and the quality of the evidence that supports each.