Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images
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) m...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:minerva_____::4a7fc696e795d19b4975debac1b964ae |
| Acceso en línea: | https://hdl.handle.net/10347/47343 |
| Access Level: | acceso abierto |
| Palabra clave: | Human-Centric Explainable Artificial Intelligence Data-to-Text Systems Ruled-based Text Generation Fuzzy Quantification Surrogate Deep Learning |
| Sumario: | 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$\pm$0.12 (out of 5) on average across the five dimensions considered. We also performed a trustworthy self-assessment procedure following the ALTAI checklist, in which it scored 4.40$\pm$0.27 (out of 5) across seven dimensions of the AI Trustworthiness Assessment List. |
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