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.

翻译：将深度学习整合到医疗保健领域能够实现个性化护理，但由于模型的不透明性也引发了信任问题。为提高透明度，我们提出了一种基于全景影像的牙龄估计系统，该系统在自然语言生成模块中结合了不透明与透明方法。该模块通过与牙科专家协作，采用基于规则的方法，生成关于年龄估计的、便于临床医生理解的文本解释。遵循该领域的最佳实践，生成的解释质量由牙科专家通过问卷进行人工验证。结果显示系统性能优异，专家在五个考量维度上的平均评分为4.77±0.12（满分5分）。我们还按照ALTAI清单执行了可信度自评估程序，在人工智能可信度评估清单的七个维度上得分为4.40±0.27（满分5分）。