ArticleInternational journal of emergency medicine2025
Implementation of an AI-assisted tele-stroke robot to optimize acute stroke care: a case series from the SEHA Healthcare Network, UAE.
Article in International journal of emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
11 authors.
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Abstract
backgroundTimely 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.
methodsThis 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.
resultsThe 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.
conclusionThe 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.
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