MEDICAL IMAGE ANALYSIS IN KNEE OSTEOARTHRITIS USING GENERATIVE AI: A COMPREHENSIVE REVIEW

Abstract

Knee osteoarthritis (KOA) is a prevalent degenerative disease characterized by cartilage deterioration, joint space narrowing, osteophyte formation, and progressive functional decline. Medical imaging modalities such as radiographs, MRI, CT, and ultrasound play a central role in KOA diagnosis, severity grading, and monitoring. Traditional computer-aided diagnosis (CAD) approaches and early deep learning models face limitations due to small datasets, annotation challenges, and variability across imaging centers. Recent advances in Generative Artificial Intelligence (GenAI), including GANs, VAEs, and diffusion models, have enabled realistic synthetic image generation, domain adaptation, super-resolution enhancement, and automated grading. This review provides a comprehensive overview of GenAI applications in KOA imaging, discusses state-of-the-art developments, highlights clinical challenges, and outlines open research problems. Our survey indicates that GenAI has strong potential to improve diagnostic consistency, mitigate data scarcity, and support precision orthopedics.