Machine learning methods in physical therapy: A scoping review of applications in clinical context

Background

Machine learning (ML) efficiently processes large datasets, showing promise in enhancing clinical practice within physical therapy.

Objective

The aim of this scoping review is to provide an overview of studies using ML approaches in clinical settings of physical therapy.

Data sources

A scoping review was performed in PubMed, EMBASE, PEDro, Cochrane, Web of Science, and Scopus.

Selection criteria

We included studies utilizing ML methods. ML was defined as the utilization of computational systems to encode patterns and relationships, enabling predictions or classifications with minimal human interference.

Data extraction and data synthesis

Data were extracted regarding methods, data types, performance metrics, and model availability.

Results

Forty-two studies were included. The majority were published after 2020 (n = 25). Fourteen studies (33.3%) were in the musculoskeletal physical therapy field, nine (21.4%) in neurological, and eight (19%) in sports physical therapy. We identified 44 different ML models, with random forest being the most used. Three studies reported on model availability. We identified several clinical applications for ML-based tools, including diagnosis (n = 14), prognosis (n = 7), treatment outcomes prediction (n = 7), clinical decision support (n = 5), movement analysis (n = 4), patient monitoring (n = 3), and personalized care plan (n = 2).

Limitation

Model performance metrics, costs, model interpretability, and explainability were not reported.

Conclusion

This scope review mapped the emerging landscape of machine learning applications in physical therapy. Despite the growing interest, the field still lacks high-quality studies on validation, model availability, and acceptability to advance from research to clinical practice.