Deciphering knee osteoarthritis diagnostic features with explainable
  artificial intelligence: A systematic review
Yun Xin Teoh Alice Othmani Siew Li Goh Juliana Usman Khin Wee Lai
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.