用可解释文本提升牙科影像年龄估计的可信度
Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images
- 融合黑箱模型与规则生成解释，输出医生易懂的推理文本
- 专家评分4.77/5，可信度评估达4.40/5，结果可靠
- 适合临床辅助诊断，尤其关注AI透明性的医疗场景
将深度学习引入医疗虽能实现个性化诊疗，但模型不透明引发信任问题。为此，我们提出一套基于全景牙科影像的年龄估计系统，结合黑箱与可解释方法，并通过自然语言生成模块输出面向临床医生的文本解释。解释内容由牙科专家参与设计，采用规则驱动方式构建。为验证生成质量，专家通过问卷对五项维度进行人工评分，平均得分为4.77±0.12（满分5分）。同时，依据ALTAI清单开展可信度自评估，在七个维度上获得4.40±0.27（满分5分）的成绩。
原文摘要 · Abstract (English)
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.
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