Exploring Rehabilitation Therapists' Knowledge and Perspectives on the Use of Artificial Intelligence and Machine Learning for Persons Post-stroke

Research Objectives: The use of technology such as robotics, gaming systems, self-monitoring apps, or other sensor-based devices in standard practice is infrequent. Due to the rapid development of artificial intelligence (AI) and machine learning (ML) applications, it is important to look at how therapists perceive AI/ML, and design applications with potential barriers in mind. to support future integration into practice. The purpose of this research project is to gain rehabilitation therapists’ perspectives on AI/ML in post-stroke assessment and intervention.

Design: This ongoing study uses a mixed methods design with surveys and focus groups. Participants engaged in a 30-minute webinar to learn about AI/ML in post-stroke rehabilitation. They also completed a survey before and after this one-time virtual meeting.

Setting: This study was completed using a video conferencing platform.

Participants: To be included, participants within the last year had to have a caseload of at least 30% of persons post-stroke, be an occupational or physical therapy practitioner in the United States (US) or Asia, and speak English.

Interventions: Not applicable.

Main Outcome Measures: Participant survey responses and qualitative analyses of focus group discussions.

Results: Preliminary analysis of the survey data indicates while therapists report knowledge of many types of AI/ML technologies, however, the frequency of use in stroke rehabilitation is limited. During the focus groups, participants discussed the importance of considering the efficiency of use and carryover outside the clinic when developing AI/ML applications. After completion of the webinar and discussion, 88% of participants reported some level of agreement with the statement "I think AI, data-driven tools can improve rehabilitation therapy".

Conclusions: The complete analysis will be used to guide the creation of AI/ML technologies within stroke rehabilitation that will assist rehabilitation therapists in optimizing care.

Author(s) Disclosures: None of the authors have any conflicts of interest to declare.