###### Abstract  
Knee osteoarthritis is a chronic, progressive disease that rapidly progresses to severe stages. Reliable and accurate diagnosis, combined with the implementation of preventive lifestyle modifications before irreversible damage occurs, can effectively protect patients from becoming an inactive population. Artificial intelligence continues to play a pivotal role in computer-aided diagnosis with increasingly convincing accuracy, particularly in identifying the severity of knee osteoarthritis according to the Kellgren-Lawrence (KL) grading scale. The primary objective of this literature review is twofold. Firstly, it aims to provide a systematic analysis of the current literature on the main artificial intelligence models used recently to predict the severity of knee osteoarthritis from radiographic images. Secondly, it constitutes a critical review of the different methodologies employed and the key elements that have improved diagnostic performance. Ultimately, this study demonstrates that the considerable success of artificial intelligence systems will reinforce healthcare professionals' confidence in the reliability of machine learning algorithms, facilitating more effective and faster treatment for patients afflicted with knee osteoarthritis. In order to achieve these objectives, a qualitative and quantitative analysis was conducted on 60 original research articles published between 1 January 2018 and 15 May 2024.  
artificial intelligence (AI); knee osteoarthritis (KOA); machine learning (ML); deep learning (DL); Kellgren-Lawrence (KL); joint space narrowing (JSN)  
11 June 202411 July 202411 July 202417 July 202411 July 202  
form of arthritis that typically affects weight-bearing joints, including the knees, hips, and feet. Knee osteoarthritis is one of the most prevalent forms of the disease. Approximately 83% of all patients with osteoarthritis suffer from knee osteoarthritis. It is defined as a loss of articular cartilage, which serves as the protective tissue of the knee joint. Articular cartilage prevents the bones from rubbing and facilitates movement. KOA is generally diagnosed using conventional radiography, Computed Tomography (CT) scans, and Magnetic Resonance Imaging (MRI). Although MRI is a popular method, standard radiography is still considered the gold standard for the assessment of knee osteoarthritis and is widely used by doctors for initial examinations. This study will present a literature review of the use of AI in knee osteoarthritis diagnosis. The objective is to identify common design principles, core technologies, and major challenges to be overcome to achieve reliable and accurate models. Furthermore, the results of this assessment will be used to propose a strategic roadmap for researchers, students, and healthcare professionals. This roadmap will guide the transition to the most accurate ML and DL models by making available the techniques used and the databases employed. To achieve this objective, the following research questions were formulated:  
RQ1 What are the principal models currently employed to design diagnostic models for knee osteoarthritis based on radiographic images? RQ2 What are the potential factors for enhancing the accuracy of traditional machine learning (ML) and deep learning (DL) models developed in recently published research articles? RQ3 What are the principal challenges and prospective avenues for further research? The remainder of this document is organized as follows: Section 2 outlines the methodology employed to conduct the literature review and evaluate the study results, to identify the design principles employed in the selected articles. Section 3 presents the results of the literature review in both qualitative and quantitative terms. It also outlines the main traditional machine learning (ML) and deep learning (DL) models employed to assess the severity of knee osteoarthritis.