<![CDATA[Introduction: Musculoskeletal disease is one of the leading global causes of disabilities and lower retirement age. Researchers and health institutions are attempting to solve the problem by improving technology within the medical field to find better ways to aid patients. One of the most impactful innovations is the usage of artificial intelligence, specifically the neural network model.Objective: This article aims to evaluate current artificial intelligence-based approaches which are presented as the solution to tackle difficulties regarding musculoskeletal condition prevention and diagnosis.Methods: This article is a literature review researched using derived qualitative research using available research materials. Sources are selected from publications where researchers propose new neural network models used in deep learning which are relevant to current health problems.Results: The currently tested clinical applications include magnetic resonance imaging (MRI) image reconstruction, joint localization, level of severity determination, knee osteoarthritis prediction, arthritis distinction, and disease-specific joint regions identification.Conclusion: Artificial intelligence in the medical field aids early prevention and diagnosis by improving efficiency, imaging quality, and diagnosis accuracy. Integrating a multidisciplinary approach is crucial to develop a precise patient-centric intervention system]]>
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