## 4 Discussion
This is because the distinction between the first two grades is generally very challenging, even for experienced medical specialists, who find it difficult to identify grade KL0 as KL1 and vice versa. As shown in Figure 4, the main guidelines proposed in the research articles can be summarized in the following steps: (i) collecting images; (ii) image processing; (iii) ROI detection/segmentation; (iv) features extraction; (v) features selection; (vi) classification and progression; and (vii) visualization. The knee OA classification process begins with database selection and image pre-processing to eliminate noise and improve image quality. The second phase consists of manual, automatic, or semi-automatic detection of the ROI. Despite its importance in improving diagnostic accuracy, this phase is not used in some studies, where the first phase is followed directly by the third phase of feature extraction. The final phase consists of diagnosing knee osteoarthritis using a classification model to identify the severity of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. In light of the preceding analysis, the reply to the last research question (RQ3) can be divided into three categories of AI challenges for future research in knee osteoarthritis identification:  
1. Dataset preparation: The initial step in the process of selecting a suitable database is to guarantee an accurate diagnosis of knee osteoarthritis from radiographic images. This can be achieved by using mixed and balanced datasets with external validation, which is a first step towards reliable training. The classification of knee osteoarthritis can be simplified by merging the KL0 and KL1 grades into a single category [13; 15; 20; 26; 48]. Although KL1 has no real significance, it reduces the model's accuracy as it is challenging to differentiate it from KL0. 2. Model selection: In addition to preparing a suitable database, the type of model chosen, hyperparameters, features extraction and selection, ROI localization, and JSN quantification are the most important factors in effectively improving the accuracy of the trained model. In light of these promising outcomes, the deployment of diverse iterations of YOLLO for real-time ROI detection and localization is becoming increasingly prevalent. 3. Visualization: The reliability of a diagnosis is no longer solely based on its accuracy; it is also based on the visualization of areas of deficiency. The orientation of new research towards a precise and justified diagnosis is analogous to that of the specialist doctors who base their final diagnosis on well-defined symptoms. This approach could therefore be used to confirm the degree of knee osteoarthritis by visualizing the key elements that have contributed to the identification of this condition. This would provide greater confidence in the developed approach. Finally, this study has some limitations. Firstly, the models compared are not tested on comparable databases since the size and nature of the datasets used during training and validation differ from one article to another. Secondly, the models that obtained low predictions did not indicate whether the training of the models used was saturated or not, bearing in mind that in the event of non-saturation, increasing the number of epochs used could improve their predictions. Finally, although the majority of evaluation methods used were based on mean accuracy, several studies used other evaluation criteria such as mean F1 score, mean recall, and mean precision [7; 38], making the results incomparable. Lastly, the Scopus index was not considered in the selection of reviewed papers, which would have enhanced the findings of this literature review. Nevertheless, this survey provides a comprehensive update for doctors and researchers, offering a global perspective on the direction of scientific research and a valuable quantitative and qualitative analysis for all scientists.