Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images
Abstract
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.
Cite
@article{arxiv.2601.12960,
title = {Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images},
author = {Ainhoa Vivel-Couso and Nicolás Vila-Blanco and María J. Carreira and Alberto Bugarín-Diz and Inmaculada Tomás and Jose M. Alonso-Moral},
journal= {arXiv preprint arXiv:2601.12960},
year = {2026}
}
Comments
This paper is a preliminary version of an accepted article in Information Systems Frontiers, Springer, Special Issue "Explainability in Human-Centric AI". Please cite the final published version of the paper, not this preprint. The final published version can be found at https://link.springer.com/article/10.1007/s10796-025-10682-3