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Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review

arXiv 2308.09380 ↗First posted 18 Aug 2023cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 144 references that are in the map. First posted on arXiv 18 Aug 2023.

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23 of 144 references.

References · 23

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11 more
  1. 2017Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approachcited by 42
  2. 2023A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?cited by 24
  3. 2019Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Datacited by 23
  4. 2016Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networkscited by 17
  5. 2020Trustworthy AI in the Age of Pervasive Computing and Big Datacited by 9
  6. 2021Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Networkcited by 6
  7. 2021Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Reviewcited by 5
  8. 2019Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysiscited by 4
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  11. 2023Confidence-Driven Deep Learning Framework for Early Detection of Knee Osteoarthritiscited by 2

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