Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images

TL;DR

This paper presents a transparent deep learning system for dental age estimation from panoramic images, incorporating natural language explanations validated by dental experts to enhance trustworthiness in healthcare AI applications.

Contribution

It introduces a novel system combining opaque and transparent methods with NLG for explainable dental age estimation, validated through expert assessments.

Findings

High expert-rated explanation quality (average 4.77/5)

Strong performance in trustworthiness assessment (average 4.40/5)

Effective integration of transparency and explainability in healthcare AI

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.