Evidence map›Paper›PMID 41821400›Full record

ReviewMultiple sclerosis (Houndmills, Basingstoke, England)2026

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

Hernan Inojosa, Rebecca Mathias, Anja Dillenseger, Isabel Voigt, Katrin Trentzsch, Julia Steinigen-Fuchs, Katrin Piehler, Stephen Gilbert, Tjalf Ziemssen

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Multiple sclerosis (Houndmills, Basingstoke, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Hernan InojosaCenter of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, TUD Dresden University of Technology, Dresden, Germany.ORCID 0000-0002-1377-836X
Rebecca MathiasElse Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.ORCID 0000-0002-3906-2415
Anja DillensegerCenter of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, TUD Dresden University of Technology, Dresden, Germany.
Isabel VoigtCenter of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, TUD Dresden University of Technology, Dresden, Germany.ORCID 0000-0003-0097-8589
Katrin TrentzschCenter of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, TUD Dresden University of Technology, Dresden, Germany.
Julia Steinigen-FuchsMember of the Ethics Committee, TUD Dresden University of Technology, Dresden, Germany.
Katrin PiehlerQuality and Medical Risk Management, Data Security, University Hospital Carl Gustav Carus Dresden, Technical University of Dresden, Dresden, Germany.
Stephen GilbertElse Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
Tjalf ZiemssenCenter of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, TUD Dresden University of Technology, Dresden, Germany.ORCID 0000-0001-8799-8202

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid rise of artificial intelligence (AI) and digital health technologies presents new opportunities for personalized care in multiple sclerosis (MS). However, implementation in routine practice is limited by regulatory hurdles, fragmented infrastructure and a lack of agile real-world evaluation methods. Living Labs (LLs) emerge as dynamic environments for advancing MS care and research, supporting early testing and iterative development of digital tools, while fostering structured collaboration among patients, clinicians, researchers and regulators. In this review, we conceptually frame LLs in MS and provide a concrete, clinic-ready implementation framework for AI-enabled application in real-world practice. Using a digital-based voice task as an exemplar with automated feature extraction, we detail integration patterns and define key performance indicators for feasibility, data quality, usability and clinical utility. We show how this co-designed model can generate decision-relevant evidence, may help shorten time-to-action and embed innovation seamlessly into clinical workflows. Finally, we align LL operations with ethical and regulatory standards and outline strategies to responsibly scale across centres.

Indexed as

Artificial IntelligenceBiomedical ResearchMultiple SclerosisDigital HealthHumansIntelligent Systemsartificial intelligencedigital healthLearning Health SystemLiving Labmultiple sclerosis

Identifiers

PMID41821400
PMCPMC13100325

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.