Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images
This addresses trust issues in AI for healthcare by enhancing model interpretability for dental clinicians, though it is incremental as it builds on existing methods with added transparency features.
The paper tackled the problem of improving transparency in deep learning-based chronological age estimation from panoramic dental images by integrating an opaque and a transparent method with a natural language generation module to produce clinician-friendly explanations, resulting in expert ratings of 4.77/5 for explanation quality and a trustworthy self-assessment score of 4.40/5.
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.