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[Neurofilament light chain as a tool for assessing disease activity and treatment response in Multiple Sclerosis].

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AI-enabled Living Labs: Accelerating innovation in multiple sclerosis care and research.

Hernan Inojosa1, Rebecca Mathias2, Anja Dillenseger1

  • 1Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, TUD Dresden University of Technology, Dresden, Germany.

Multiple Sclerosis (Houndmills, Basingstoke, England)
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PubMed
Summary

Living Labs (LLs) offer a framework for integrating artificial intelligence (AI) and digital health in multiple sclerosis (MS) care. This approach facilitates real-world testing and evaluation of innovative digital tools for personalized patient management.

Keywords:
Learning Health SystemLiving Labartificial intelligencedigital healthmultiple sclerosis

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Area of Science:

  • Digital Health
  • Artificial Intelligence
  • Neuroscience

Background:

  • Artificial intelligence (AI) and digital health technologies offer potential for personalized multiple sclerosis (MS) care.
  • Routine implementation faces challenges including regulatory issues, infrastructure fragmentation, and limited real-world evaluation methods.

Purpose of the Study:

  • To conceptually frame Living Labs (LLs) for advancing MS care and research.
  • To provide a practical framework for implementing AI-enabled digital tools in real-world clinical settings.
  • To detail integration patterns and define key performance indicators for AI applications in MS.

Main Methods:

  • Conceptual review and framework development for Living Labs in MS.
  • Exemplar application using a digital voice task with automated feature extraction.
  • Definition of key performance indicators for feasibility, data quality, usability, and clinical utility.
  • Alignment of LL operations with ethical and regulatory standards.

Main Results:

  • Living Labs provide dynamic environments for iterative development and testing of digital tools.
  • A co-designed model can generate decision-relevant evidence for AI implementation.
  • Integration of AI tools can shorten time-to-action and embed innovation into clinical workflows.

Conclusions:

  • Living Labs offer a viable model for advancing AI-driven personalized care in multiple sclerosis.
  • The proposed framework supports ethical and regulatory compliance for scaling innovations.
  • Structured collaboration within LLs is key to successful digital health integration in MS practice.