Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions
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 are...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
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| Online Access: | https://doi.org/10.1186/s12984-025-01605-z |
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| author | Giovanna Nicora Samuele Pe Gabriele Santangelo Lucia Billeci Irene Giovanna Aprile Marco Germanotta Riccardo Bellazzi Enea Parimbelli Silvana Quaglini |
| author_facet | Giovanna Nicora Samuele Pe Gabriele Santangelo Lucia Billeci Irene Giovanna Aprile Marco Germanotta Riccardo Bellazzi Enea Parimbelli Silvana Quaglini |
| author_sort | Giovanna Nicora |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6192c0b2f6284d439f0638942029cb91 |
| institution | DOAJ |
| issn | 1743-0003 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj-art-6192c0b2f6284d439f0638942029cb912025-08-20T03:10:11ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032025-04-0122112010.1186/s12984-025-01605-zSystematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directionsGiovanna Nicora0Samuele Pe1Gabriele Santangelo2Lucia Billeci3Irene Giovanna Aprile4Marco Germanotta5Riccardo Bellazzi6Enea Parimbelli7Silvana Quaglini8Department of Electrical, Computer and Biomedical Engineering, University of PaviaDepartment of Electrical, Computer and Biomedical Engineering, University of PaviaDepartment of Electrical, Computer and Biomedical Engineering, University of PaviaInstitute of Clinical Physiology, National Research Council of Italy (CNR-IFC)Neuromotor Rehabilitation Department, IRCCS Fondazione Don Carlo Gnocchi ONLUSNeuromotor Rehabilitation Department, IRCCS Fondazione Don Carlo Gnocchi ONLUSDepartment of Electrical, Computer and Biomedical Engineering, University of PaviaDepartment of Electrical, Computer and Biomedical Engineering, University of PaviaDepartment of Electrical, Computer and Biomedical Engineering, University of PaviaAbstract 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.https://doi.org/10.1186/s12984-025-01605-zArtificial intelligenceDeep learningPatient assessmentPhysical therapyCognitiveGait |
| spellingShingle | Giovanna Nicora Samuele Pe Gabriele Santangelo Lucia Billeci Irene Giovanna Aprile Marco Germanotta Riccardo Bellazzi Enea Parimbelli Silvana Quaglini Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions Journal of NeuroEngineering and Rehabilitation Artificial intelligence Deep learning Patient assessment Physical therapy Cognitive Gait |
| title | Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions |
| title_full | Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions |
| title_fullStr | Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions |
| title_full_unstemmed | Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions |
| title_short | Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions |
| title_sort | systematic review of ai ml applications in multi domain robotic rehabilitation trends gaps and future directions |
| topic | Artificial intelligence Deep learning Patient assessment Physical therapy Cognitive Gait |
| url | https://doi.org/10.1186/s12984-025-01605-z |
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