Voices of Recovery: Patients’ Experiences with AI-Assisted Stroke Rehabilitation
Keywords:
AI-assisted rehabilitation, stroke recovery, patient experience, human–technology interaction, qualitative study, neurorehabilitationAbstract
Objective: This study aims to explore the lived experiences of stroke survivors engaging in AI-assisted rehabilitation.
Methods and Materials: This qualitative study was conducted with 14 stroke survivors undergoing AI-assisted rehabilitation at the York Rehab Center in Richmond Hill, Canada. Participants were recruited using purposive sampling, and data collection was carried out through in-depth, semi-structured interviews. Interviews lasted 45–60 minutes and were audio-recorded, transcribed verbatim, and coded using NVivo 14 software. Thematic analysis was employed following Braun and Clarke’s six-phase framework. The study continued until theoretical saturation was achieved. Ethical considerations including informed consent and confidentiality were rigorously maintained throughout the research process.
Findings: Analysis revealed four main themes: (1) Human–Technology Interaction, including trust in AI and interface usability; (2) Emotional and Psychological Response, encompassing motivation, emotional bonding with technology, and performance anxiety; (3) Perceived Effectiveness of Rehabilitation, including functional improvement, personalized feedback, and therapy comparison; and (4) Social and Institutional Context, focusing on relationships with therapists, digital equity, and cultural influences. Patients generally found the AI systems to be engaging and supportive of their physical recovery. However, emotional detachment, dependence, and accessibility challenges emerged as concerns. Participants emphasized the need for human involvement alongside AI systems to ensure emotional and motivational support.
Conclusion: AI-assisted rehabilitation was perceived as a promising and effective complement to traditional therapy, enhancing functional outcomes and patient engagement. However, its full potential lies in hybrid models that integrate human empathy with technological precision.
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Copyright (c) 2025 James Ma, Seyed Alireza Saadati (Author)
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