Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation

Abstract

Musculoskeletal rehabilitation is an important aspect of healthcare that involves the treatment and management of injuries and conditions affecting the muscles, bones, joints, and related tissues. Clinical decision-making in musculoskeletal rehabilitation involves complex and multifactorial considerations that can be challenging for healthcare professionals. Machine learning and deep learning techniques have the potential to enhance clinical judgement in musculoskeletal rehabilitation by providing insights into complex relationships between patient characteristics, treatment interventions, and outcomes. These techniques can help identify patterns and predict outcomes, allowing for personalized treatment plans and improved patient outcomes. In this investigation, we explore the various applications of machine learning and deep learning in musculoskeletal rehabilitation, including image analysis, predictive modelling, and decision support systems. We also examine the challenges and limitations associated with implementing these techniques in clinical practice and the ethical considerations surrounding their use. This investigation aims to highlight the potential benefits of using machine learning and deep learning in musculoskeletal rehabilitation and the need for further research to optimize their use in clinical practice.