Implementation of an AI-assisted tele-stroke robot to optimize acute stroke care: a case series from the SEHA Healthcare Network, UAE

Background: Timely access to a neurologist is essential for optimal management of acute stroke. To address disparities in neurological care within the SEHA Healthcare Network, the LEO360® AI-assisted tele-stroke robot was implemented at Sheikh Tahnoon bin Mohammed Medical City (STMC) and Al Tawam Hospital. Methods: This case series presents six patients evaluated remotely via the LEO360® platform. Key metrics included consultation time, transfer rates, and system performance. Data were contextualized using pre-implementation benchmarks, and both AI functionalities and telepresence capabilities were analyzed. Challenges, ethical considerations, and system limitations were also examined. Results: The average neurologist consultation time was 10.7 minutes. In 80% of cases, unnecessary interfacility transfers were avoided. The integration of AI-assisted decision support enhanced assessment efficiency and diagnostic confidence. Conclusion: The LEO360® tele-stroke system demonstrates strong potential to improve access, efficiency, and accuracy in acute stroke management. Its successful implementation underscores the scalability of AI-assisted telemedicine in regions facing neurology workforce shortages, offering a sustainable model for acute neurological care.