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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.12960 |
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| _version_ | 1866917210065207296 |
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| author | Vivel-Couso, Ainhoa Vila-Blanco, Nicolás Carreira, María J. Bugarín-Diz, Alberto Tomás, Inmaculada Alonso-Moral, Jose M. |
| author_facet | Vivel-Couso, Ainhoa Vila-Blanco, Nicolás Carreira, María J. Bugarín-Diz, Alberto Tomás, Inmaculada Alonso-Moral, Jose M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12960 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images Vivel-Couso, Ainhoa Vila-Blanco, Nicolás Carreira, María J. Bugarín-Diz, Alberto Tomás, Inmaculada Alonso-Moral, Jose M. Computation and Language 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. |
| title | Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.12960 |