Large language model bias auditing for periodontal diagnosis using an ambiguity-probe methodology: a pilot study
Teerachate Nantakeeratipat · 2026 · Frontiers in Digital Health
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BackgroundLarge Language Models (LLMs) in healthcare holds immense promise yet carries the risk of perpetuating social biases. While artificial intelligence (AI) fairness is a growing concern, a gap exists in understanding how these models perform under conditions of clinical ambiguity, a common feature in real-world practice.MethodsWe conducted a study using an ambiguity-probe methodology with a set of 42 sociodemographic personas and 15 clinical vignettes based on the 2018 classification of periodontal diseases. Ten were clear-cut scenarios with established ground truths, while five were int
Abstract by Teerachate Nantakeeratipat, Frontiers in Digital Health (2026) — licensed CC BY 4.0.
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Metadata source: DOAJ · DOI 10.3389/fdgth.2025.1687820