Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review
It addresses the lack of interpretability in AI models for knee OA diagnosis, which is a critical problem for clinicians and patients, but is incremental as it reviews existing methods rather than proposing new ones.
This paper conducted the first systematic review of explainable AI (XAI) techniques for diagnosing knee osteoarthritis, highlighting their potential to improve transparency and trust in AI models for clinical use.
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.