Collaborative AI for precision neurorehabilitation: a roadmap
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Precision rehabilitation seeks to improve care for individuals by identifying personalized treatments that enhance both the efficacy and efficiency of care. Rapid advances in artificial intelligence (AI) and data-driven methodologies stand to greatly enhance precision rehabilitation methods, particularly when paired with clinical expertise and patient-driven goals. Although AI has been successfully integrated into healthcare in fields such as precision oncology, the dynamic, multi-faceted, complex nature of rehabilitation require a different approach than standard predictive models. Here, we argue that precision rehabilitation may benefit from collaborative AI, in which humans and AI work together with AI distilling complex data that humans can use as part of their nuanced decision-making process. We review the current landscape of precision rehabilitation and explore how collaborative AI, and AI in general, can advance the field. We begin by outlining a roadmap for a general collaborative AI system in rehabilitation, noting four key challenges that must be addressed. Next, we examine three existing precision rehabilitation frameworks that have been developed independently by several of the co-authors, which share four common key elements of the roadmap. We then describe examples of how AI has already been applied to specific aspects of complex rehabilitation scenarios, as these may be integrated into larger collaborative AI models. We follow this with a discussion of how to make AI-based precision rehabilitation a reality, with an emphasis on the large data required for these models to make accurate predictions, as well as potential ethical issues. Finally, we conclude with recommendations for future directions. Ultimately, collaborative AI promises to transform rehabilitation by leveraging vast, diverse datasets to create individualized digital profiles, which can first be used to simulate the effects of rehabilitation interventions in silico, maximizing impacts before real-world implementation. By merging personalized rehabilitation strategies based on empirical evidence with the depth and complexity of clinical knowledge and reasoning, collaborative AI holds promise as a powerful tool to help clinicians advance patient care and improve long-term outcomes.
Date Issued
2025-12-30
Date Acceptance
2025-11-13
Citation
Journal of NeuroEngineering and Rehabilitation, 2025, 22
ISSN
1743-0003
Publisher
BMC
Journal / Book Title
Journal of NeuroEngineering and Rehabilitation
Volume
22
Copyright Statement
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41310676
PII: 10.1186/s12984-025-01810-w
Subjects
Artificial intelligence
Bayesian modeling
Collaborative AI
Digital twin
Large language models
Neurorehabilitation
Precision medicine
Reinforcement learning
Publication Status
Published
Coverage Spatial
England
Article Number
ARTN 269
Date Publish Online
2025-11-27