This paper addresses the unique challenges associated with uncertainty quantification in AI models when applied to patient-facing contexts within healthcare. Unlike traditional eXplainable Artificial Intelligence (XAI) methods tailored for model developers or domain experts, additional considerations of communicating in natural language, its presentation and evaluating understandability are necessary. We identify the challenges in communication model performance, confidence, reasoning and unknown knowns using natural language in the context of risk prediction. We propose a design aimed at addressing these challenges, focusing on the specific application of in-vitro fertilisation outcome prediction.

翻译：本文探讨了在医疗健康领域中，当人工智能模型应用于面向患者场景时，与不确定性量化相关的独特挑战。与传统为模型开发者或领域专家定制的可解释人工智能（XAI）方法不同，此处还需额外考虑如何以自然语言进行传达、其呈现方式以及可理解性评估。我们识别出在风险预测背景下，使用自然语言传达模型性能、置信度、推理过程及未知已知信息时所面临的挑战。我们提出了一项旨在应对这些挑战的设计方案，重点关注体外受精结果预测这一具体应用场景。