Publication: Large language model bias auditing for periodontal diagnosis using an ambiguity-probe methodology: a pilot study
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Issued Date
2026-01-01
Resource Type
eISSN
2673253X
Scopus ID
2-s2.0-105027899909
Journal Title
Frontiers in Digital Health
Volume
7
Rights Holder(s)
SCOPUS
Bibliographic Citation
Frontiers in Digital Health Vol.7 (2026)
Suggested Citation
Nantakeeratipat T. Large language model bias auditing for periodontal diagnosis using an ambiguity-probe methodology: a pilot study. Frontiers in Digital Health Vol.7 (2026). doi:10.3389/fdgth.2025.1687820 Retrieved from: https://hdl.handle.net/20.500.14740/55332
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Abstract
Background: Large 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. Methods: We 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 intentionally ambiguous. OpenAI's GPT-4o and Google's Gemini 2.5 Pro were prompted to provide periodontal stage and grade assessments using 630 vignette-persona combinations per model. Results: In clear-cut scenarios, GPT-4o demonstrated significantly higher combined (stage and grade) accuracy (70.5%) than Gemini Pro (33.3%). However, a robust fairness analysis using cumulative link models with false discovery rate correction revealed no statistically significant sociodemographic bias in either model. This finding held true across both clear-cut and ambiguous clinical scenarios. Conclusion: To our knowledge, this is among the first study to use simulated clinical ambiguity to reveal the distinct ethical fingerprints of LLMs in a dental context. While LLM performance gaps exist, our analysis decouples accuracy from fairness, demonstrating that both models maintain sociodemographic neutrality. We identify that the observed errors are not bias, but rather diagnostic boundary instability. This highlights a critical need for future research to differentiate between these two distinct types of model failure to build genuinely reliable AI.