Beyond binary dentistry: Should artificial intelligence think more like clinicians? [Editorial]

Turky, Mohammed and Dummer, Paul M. H. ORCID: https://orcid.org/0000-0002-0726-7467 2026. Beyond binary dentistry: Should artificial intelligence think more like clinicians? [Editorial]. European Journal Of Oral Sciences , e70155. 10.1111/eos.70155

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Abstract

Artificial intelligence (AI) is increasingly influencing dental diagnosis and clinical decision-making, yet many AI systems continue to represent disease and treatment outcomes through discrete categories. Although binary classification has contributed substantially to diagnostic performance, it does not always reflect how clinicians reason in practice. Dental decisions are frequently made under incomplete information, biological variability, and competing clinical findings, requiring dentists to weigh probabilities, alternative explanations, and degrees of confidence rather than simply choosing between two outcomes. This short communication argues that future dental AI should better represent this inherent clinical uncertainty. Fuzzy logic provides one possible conceptual framework, allowing partial membership and graded states rather than forcing complex biological conditions into rigid categories. Its potential value in dentistry extends beyond computation: it may help AI communicate uncertainty in ways that are more consistent with clinical reasoning. Such systems could provide graded diagnostic confidence, competing possibilities, or degrees of treatment suitability, rather than presenting apparently definitive answers. This approach may also support explainability and reduce inappropriate reliance on algorithmic recommendations. The future of dental AI may therefore depend not only on improving predictive accuracy, but also on representing the grey zones in which much of clinical dentistry takes place.

Item Type:	Short Communication
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Dentistry
ISSN:	0029-845X
Last Modified:	22 Oct 2026 11:30
URI:	https://orca.cardiff.ac.uk/id/eprint/190379

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