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. 2025 Apr 9;22(1):79.
doi: 10.1186/s12984-025-01605-z.

Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions

Giovanna Nicora  1 , Samuele Pe  2 , Gabriele Santangelo  2 , Lucia Billeci  3 , Irene Giovanna Aprile  4 , Marco Germanotta  4 , Riccardo Bellazzi  2 , Enea Parimbelli #  2 , Silvana Quaglini #  2
Affiliations

Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions

Giovanna Nicora et al. J Neuroeng Rehabil. .

Abstract

Robotic technology is expected to transform rehabilitation settings, by providing precise, repetitive, and task-specific interventions, thereby potentially improving patients' clinical outcomes. Artificial intelligence (AI) and machine learning (ML) have been widely applied in different areas to support robotic rehabilitation, from controlling robot movements to real-time patient assessment. To provide an overview of the current landscape and the impact of AI/ML use in robotics rehabilitation, we performed a systematic review focusing on the use of AI and robotics in rehabilitation from a broad perspective, encompassing different pathologies and body districts, and considering both motor and neurocognitive rehabilitation. We searched the Scopus and IEEE Xplore databases, focusing on the studies involving human participants. After article retrieval, a tagging phase was carried out to devise a comprehensive and easily-interpretable taxonomy: its categories include the aim of the AI/ML within the rehabilitation system, the type of algorithms used, and the location of robots and sensors. The 201 selected articles span multiple domains and diverse aims, such as movement classification, trajectory prediction, and patient evaluation, demonstrating the potential of ML to revolutionize personalized therapy and improve patient engagement. ML is reported as highly effective in predicting movement intentions, assessing clinical outcomes, and detecting compensatory movements, providing insights into the future of personalized rehabilitation interventions. Our analysis also reveals pitfalls in the current use of AI/ML in this area, such as potential explainability issues and poor generalization ability when these systems are applied in real-world settings.

Keywords: Artificial intelligence; Cognitive; Deep learning; Gait; Movement; Patient assessment; Physical therapy; Sensor; Stroke; Trauma.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
PRISMA diagram for systematic review
Fig. 2
Fig. 2
Most prevalent aims for which AI/ML is used in rehabilitation robotics
Fig. 3
Fig. 3
a For each “aim” category, the number of papers using AI/ML for the specific aim is reported. b For each AI/ML algorithm, the number of papers using the specific algorithm is reported. c For each input data type, the number of papers indicating that input data for their AI/ML system is reported
Fig. 3
Fig. 3
a For each “aim” category, the number of papers using AI/ML for the specific aim is reported. b For each AI/ML algorithm, the number of papers using the specific algorithm is reported. c For each input data type, the number of papers indicating that input data for their AI/ML system is reported
See this image and copyright information in PMC

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