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Applications and effectiveness of artificial intelligence and machine learning techniques in physiotherapy

Abstract:

Introduction: The convergence of artificial intelligence (AI), machine learning (ML), and physiotherapy are constantly evolving fields that have led to significant advancements in the diagnosis, treatment, and monitoring of patients. Objective: The objective of this systematic literature review (SLR) is to comprehensively analyze the scientific literature from the last 5 years to identify technological advances and approaches with trends towards the fields of both AI and physiotherapy, gathering valuable information for specialists. Material and Methods: The PRISMA methodology was employed to conduct a systematic analysis of 94 articles that met the inclusion and exclusion criteria defined by the authors, ensuring quality assessment based on predetermined criteria. Results: Developed countries lead research in the field, with India emerging as a prominent actor. Various techniques were identified, ranging from basic algorithms to deep learning, emphasizing continuous progress. The influence of AI and ML extends from radiological diagnosis to the simulation of clinical assessments, providing benefits in both clinical effectiveness and socio-economic aspects. The technology drives personalized therapies and remote monitoring, transforming physiotherapeutic practices. Conclusions: The findings of this review have significant implications for physiotherapy practices and policies, emphasizing the need for increased research in developing countries and the implementation of advanced technological approaches.