mlsanityData to 7 Oct 2026

Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images

arXiv 2601.12960 ↗First posted 19 Jan 2026cs.CLcited by 0

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Abstract (arXiv)

Integrating deep learning into healthcare enables personalized care but raises trust issues due to model opacity. To improve transparency, we propose a system for dental age estimation from panoramic images that combines an opaque and a transparent method within a natural language generation (NLG) module. This module produces clinician-friendly textual explanations about the age estimations, designed with dental experts through a rule-based approach. Following the best practices in the field, the quality of the generated explanations was manually validated by dental experts using a questionnaire. The results showed a strong performance, since the experts rated 4.77+/-0.12 (out of 5) on average across the five dimensions considered. We also performed a trustworthy self-assessment procedure following the ALTAI checklist, in which it scored 4.40+/-0.27 (out of 5) across seven dimensions of the AI Trustworthiness Assessment List.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 63 references that are in the map. First posted on arXiv 19 Jan 2026.

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References · 13

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  1. 2020A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Imagescited by 35

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