Published Paper Details:

: Osteoarthritis (OA), also known as degenerative joint disease, is a chronic condition characterized by the gradual deterioration of cartilage within the joints. This degeneration leads to pain, stiffness, and reduced joint function, significantly impacting the quality of life of those affected. OA is closely associated with aging, as its symptoms tend to worsen over time, causing long-term discomfort and structural changes to the affected joints. As the disease progresses, it can lead to severe pain, disability, and a significant loss of mobility, making it one of the leading causes of disability worldwide. OA primarily affects weight-bearing joints such as the knees, hips, and spine, but can also involve smaller joints in the hands and feet.Despite the widespread prevalence of osteoarthritis, the best available treatments primarily focus on pain management and improving joint function but do not offer a cure for the degenerative process. Current treatment options include medications, physical therapy, and in severe cases, joint replacement surgery. However, these therapies only address the symptoms, and they cannot stop or reverse the progression of the disease. As the global population ages, OA is expected to become an even more significant public health issue, with an increasing number of individuals experiencing its debilitating effects.In recent years, significant advancements in Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL), have shown promise in improving the accuracy and efficiency of osteoarthritis detection and diagnosis. AI techniques, when applied to medical imaging, clinical data analysis, and predictive modeling, offer a powerful tool to detect early signs of OA, potentially before the onset of symptoms. By analyzing complex datasets from medical images (such as X-rays, MRIs, and CT scans) and patient-specific clinical data, AI can identify subtle patterns and predict the risk of progression. This early detection could enable clinicians to intervene sooner, potentially preventing or slowing down the disease's progression.This review paper aims to explore the state-of-the-art AI techniques applied to the detection and diagnosis of degenerative joint disease, focusing on the most innovative image-based detection methods, clinical data analysis, and predictive modeling techniques. It will provide an overview of how AI is enhancing the diagnostic accuracy of OA, examining both established and emerging approaches. The paper also identifies key open problems and research gaps, including the need for better data quality, model generalizability, and integration of AI tools into clinical workflows. Furthermore, it discusses the challenges associated with incorporating AI into osteoarthritis diagnostics, such as data privacy concerns, regulatory hurdles, and the need for interpretability of AI models to ensure trust and adoption by healthcare professionals.Finally, this paper will look at the future prospects of AI in osteoarthritis diagnosis and management, including the potential for personalized medicine, more effective treatment strategies, and improved patient outcomes. As AI technologies continue to evolve, they hold the potential to revolutionize the way osteoarthritis is detected, treated, and managed, offering hope for better care and quality of life for millions of people worldwide.