Existing artificial intelligence (AI) models for diagnosing knee osteoarthritis (OA) have faced criticism for their lack of transparency and interpretability, despite achieving medical-expert-like performance. This opacity makes them challenging to trust in clinical practice. Recently, explainable artificial intelligence (XAI) has emerged as a specialized technique that can provide confidence in the model's prediction by revealing how the prediction is derived, thus promoting the use of AI systems in healthcare. This paper presents the first survey of XAI techniques used for knee OA diagnosis. The XAI techniques are discussed from two perspectives: data interpretability and model interpretability. The aim of this paper is to provide valuable insights into XAI's potential towards a more reliable knee OA diagnosis approach and encourage its adoption in clinical practice.

翻译：现有用于诊断膝关节骨关节炎（OA）的人工智能（AI）模型尽管表现出与医学专家相当的性能，但因缺乏透明度和可解释性而受到批评。这种不透明性使得它们难以在临床实践中被信任。近年来，可解释人工智能（XAI）作为一种专门技术应运而生，它通过揭示预测结果的推导过程，为模型预测提供可信度，从而推动AI系统在医疗领域的应用。本文首次对用于膝关节OA诊断的XAI技术进行了综述。从数据可解释性和模型可解释性两个角度对XAI技术进行了讨论。本文旨在深入探讨XAI在实现更可靠的膝关节OA诊断方法方面的潜力，并鼓励其在临床实践中的应用。