Programpunkter

Workshop

Region Skåne shares its approach to building organizational capacity for AI in healthcare, including strategy development, governance structures, and practical experiences from implementation work.

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Digital triage promises faster, smarter care – but how well does it really work? Behind every accuracy claim lies a maze of methods, metrics, and assumptions that make comparison anything but simple. Our presentation reveal what performance numbers truly mean, and why they often don’t tell the full story.

The ASAP Stroke project explores how artificial intelligence (AI) can support early stroke assessment and triage in prehospital settings. The project follows two tracks: traditional AI using structured data such as registry data, vital signs, and clinical observations; and video-based analysis using machine learning (ML) to assess neurological features associated with stroke from patient videos recorded in ambulances. Synthetic video data are used to enable model development while preserving patient privacy, with initial studies demonstrating promising performance in assessing eye movement abnormalities and neglect. By integrating these data sources into an AI-based clinical decision support system (CDSS), ASAP Stroke aims to improve stroke identification, distinguish large vessel occlusions (LVO) and guide optimal care pathways. Overall, ASAP Stroke seeks to strengthen prehospital decision-making, bridge prehospital and in-hospital stroke assessment, reduce time-to-treatment and improve patient outcomes.

Symposia/Panel

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Symposia/Panel

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Workshop

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