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

arXiv: https://arxiv.org/abs/2308.09380 · HTML: https://mlsanity.com/paper/2308.09380/

## 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.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach](https://mlsanity.com/paper/1710.10589/) (2017)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data](https://mlsanity.com/paper/1904.06236/) (2019)
- [Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1609.02469/) (2016)
- [Trustworthy AI in the Age of Pervasive Computing and Big Data](https://mlsanity.com/paper/2002.05657/) (2020)
- [Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network](https://mlsanity.com/paper/2106.14292/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis](https://mlsanity.com/paper/1908.07736/) (2019)
- [Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution](https://mlsanity.com/paper/2201.04318/) (2022)
- [Transformer with Selective Shuffled Position Embedding and Key-Patch Exchange Strategy for Early Detection of Knee Osteoarthritis](https://mlsanity.com/paper/2304.08364/) (2023)
- [Confidence-Driven Deep Learning Framework for Early Detection of Knee Osteoarthritis](https://mlsanity.com/paper/2303.13203/) (2023)

## 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.