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Artificial intelligence and gamified interventions in stroke rehabilitation: a systematic review of clinical applications and technological advances

neuroscience
neurology
human-computer-interaction
rehabilitation

Abstract

Background: Artificial intelligence (AI) and gamification are emerging as innovative tools in stroke rehabilitation, offering personalized, engaging, and in some cases, remotely deliverable interventions. These technologies aim to enhance functional recovery, patient adherence, and scalability of therapy. This systematic review aims to evaluate the clinical effectiveness, technological features, and implementation models of artificial intelligence (AI)–enhanced gamified interventions in stroke rehabilitation.

Methods: Following PRISMA 2020 guidelines, a comprehensive search across five databases, PubMed, Scopus, Embase, CINAHL, and Web of Science, identified 22 studies (2010–2025), including randomized controlled trials, pilot studies, simulation models, and protocols. Interventions were categorized by delivery model (home-based, clinic-based, or simulated), and outcomes included motor recovery, balance, swallowing, adherence, and usability.

Results: Most studies demonstrated significant improvements in upper limb function, balance, or cognitive engagement. AI applications such as reinforcement learning and generative adversarial networks (GANs) enable adaptive difficulty and personalization. While several systems supported home-based telerehabilitation, others were limited to clinical settings or early-stage development. Adherence rates were high in gamified systems (>80%), and usability was well-rated. The risk of bias was low in most RCTs.

Conclusion: AI-enhanced gamified interventions show promise in improving stroke recovery outcomes. However, delivery models vary widely, and not all systems are telerehabilitation-enabled. Future research should emphasize long-term outcomes, cost-effectiveness, and standardization to ensure clinical utility and equitable scalability.

Prospero Registration: (https://www.crd.york.ac.uk/PROSPERO/view/CRD420250631797)

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