Integration of Artificial Intelligence for Diagnostic Methods in Musculoskeletal Conditions: A Systematic Review

Abstract

Artificial intelligence (AI) is a multi-disciplinary area of research focused on understanding, simulating, and replicating intelligence and cognitive functions by applying computational, mathematical, logical, mechanical, and biological principles and technologies. The concept of AI involves investigating and exploring human intelligence and creating artificial computers that use intelligent algorithms to replicate human intelligence. With the appearance of machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), the key AI techniques that are particularly effective in capturing feature items and learning, AI has evolved into a powerful approach in image analysis. AI may enable more precise evaluations of musculoskeletal impairments, reducing the likelihood of misdiagnosis and improving treatment outcomes for patients. With improved diagnostic capabilities, physiotherapists can create tailored rehabilitation programs that cater to the specific needs and conditions of individual patients. This study aimed to explore and evaluate the integration of AI technologies in diagnostic methods to enhance assessment accuracy. A systematic review was conducted from available literature on AI applications in musculoskeletal diagnostics. Available articles from 2015 to 2025 were included in the study. Analysis of current research's trends, advantages, constraints, and gaps was recognized. This study highlights the promising role of AI technologies in enhancing the accuracy and efficiency of musculoskeletal diagnostics. The integration of AI has the potential to revolutionize diagnostic methods, offering more precise assessments and reducing the likelihood of misdiagnosis. The issue of deploying AI tools for diagnostic purposes needs more attention.

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