This review aims to evaluate the potential of AI and ML in improving the accuracy and efficiency of physiotherapy assessments. It explores AI-driven tools capable of analyzing large datasets for movement patterns, predicting patient outcomes, and providing objective measures for gait, posture, and range of motion. Additionally, the study proposes a framework for integrating AI tools into routine clinical practice, ensuring they enhance, rather than replace, clinician expertise.
A systematic review of the literature was conducted to identify AI and ML applications in physiotherapy assessments. The databases PubMed, CINAHL, Cochrane Central, Medline, and Embase were searched for articles published between 2010 and 2024 to capture recent technological advances. The search used terms such as "AI in physiotherapy," "machine learning in rehabilitation," and "gait analysis with AI." Studies with fewer than 20 participants, lacking empirical testing, or not directly related to physiotherapy outcomes were excluded. Descriptive statistics were used to summarize diagnostic accuracy and time efficiency of AI tools. Where possible, data were pooled to identify trends across physiotherapy applications such as gait analysis and rehabilitation monitoring.
Artificial Intelligence (AI) is increasingly enhancing physical therapy assessments by introducing tools that objectively evaluate patient progress, personalize treatment plans, and facilitate remote monitoring.The QUALITOUCH Activity Index utilizes AI to analyze patient-reported outcomes, aiding in the assessment of conditions like chronic lower back pain and knee osteoarthritis (Zaugg et al., 2022) Additionally, deep learning frameworks have been developed to automate the evaluation of rehabilitation exercises, providing therapists with precise metrics to tailor interventions effectively (Liao et al., 2019). Moreover, AI-driven motion analysis software offers real-time feedback on movement quality, enhancing the effectiveness of therapy sessions ( Zsarnoczky-Dulhazi et al.,2024). However, despite these advancements, challenges such as the need for adequate training and concerns about data privacy remain barriers to widespread AI adoption in rehabilitation settings (Alsobhi et al., 2022).
AI and machine learning offer significant potential to improve physiotherapy assessments by enhancing precision, efficiency, and personalization. However, challenges such as clinician training, data privacy, and system integration must be addressed for widespread clinical adoption. Further research is needed.
Clinical Practice: AI improves precision, offers real-time feedback, and enables remote monitoring for better outcomes.
Management: AI optimizes clinic resources, automating routine tasks and enhancing decision-making.
Education: Training on AI tools will be essential for future and current practitioners.
Policy: Standardized protocols, data privacy measures, and updated reimbursement policies will be needed.
This will lead to more efficient, effective, and accessible physiotherapy care.
Physiotherapy Assessment
Machine Learning
