Deep Learning and Explainable AI for Knee Osteoarthritis Diagnosis: A Comprehensive Review of Multitask Models, Fairness, and Diagnostic Interpretability

Abstract

Osteoarthritis severely affects the individual quality of life at a large scale of the disease through its extensive disease of the knee. This is because by combining Deep learning (DL) systems with explainable artificial intelligence (XAI) techniques, it is possible to generate automatic interpretations of accurate diagnostic models in the classification of knee osteoarthritis. Current evaluation of knee OA analytic development introduces evaluations of the way in which DL and XAI approaches to knee evaluation assignments are carried out as well as assessing the datasets and significant research results applied. The paper investigates key factors, which are both related to multitask learning and feature selection and deep neural networks and fairness considerations in orthopedic imaging AI applications. The section discusses how different data types such as medical scans with gait patterns and medical records of the patient help in resolving the diagnosis issues and problems with disease classifications. The review draws an assessment of the current problems in AIbased knee OA evaluation procedures since it addresses the existing concerns on bias prevention and clarity of interpretation and then outlines possible ways to improve AI evaluation systems in terms of reliability and fairness.

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