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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • Ali Hassan,
  • Tiago Moreira,
  • Ahmed Hassan,
  • Ahmed Samir Farw,
  • Muneer Al Marzooqi,
  • Neema Francis,
  • Roxanne Roby,
  • Sonia Lamichhane,
  • Bita Lyons,
  • Keyvan Zeynali,
  • Thiagarajan Jaiganesh

摘要

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.