Progress in Digital Twin Approaches in Human Multiple Sclerosis
Current approaches related to digital twins (DTs) in multiple sclerosis (MS) show significant advancements in areas like data integration and predictive modeling. However, these innovations have not yet been fully implemented in clinical systems, with functionalities for real-time data exchange and model updating remaining limited.
- Tags
- #MSResearch
- Most relevant to
- —
- Human efficacy
- Not established from this source
- Mechanism
- Not reported
- Evidence strength
- Moderate
- Outcome
- Not reported
Why it matters
This research highlights the progression of digital health technology in MS, which could enhance patient care and personalized treatments. Identifying existing barriers is essential for future integration into clinical practice.
What this does not prove
While progress is noted, core functionalities of DTs are not fully operational in clinical workflows, and issues like data fragmentation and lack of standardization still pose challenges.
Next milestone
No next milestone was established from the available source.
Study facts
- Study design
- Systematic review
- Participants / samples
- Not reported
- Randomised
- Not reported
- Controlled
- Not reported
- Primary endpoint met
- Not reported
- Relevant MS type
- Not reported
- Publication date
- Not reported
- Evidence reviewed
- Abstract only
- Regulatory approval
- Not reported
- Research areas
- Not classified
- Biological target
- Not reported
- Pathway
- Not reported
- Population
- Not reported
- Size of effect
- Not reported
- Secondary endpoints
- Not reported
- MRI endpoints
- Not reported
- Biomarkers
- Not reported
- Safety
- Not reported
Original sources
- Primary evidenceDigital Twins in Clinical Neurology: From Concept to Clinical Application in Multiple Sclerosis. ↗DOI: 10.1111/ene.70791
Supporting passages (5)
study designPublication types: Journal Article; Review
subjectsMeSH terms: Humans; Multiple Sclerosis; Neurology; Digital Health
findingsRESULTS: Current DT-related approaches in MS demonstrate substantial progress across complementary development tracks, including multimodal data integration, predictive modelling, digital biomarkers, mechanistic simulations, virtual-patient frameworks and in silico trials. These capabilities represent important building blocks of DT development but have not yet been fully integrated into continuously operating clinical systems. Core DT functionalities, including continuous bidirectional data exchange, real-time model updating and closed-loop interaction with clinical workflows, remain limited. Key challenges include data fragmentation, limited interoperability, lack of standardised validation, and regulatory, ethical and economic constraints.
limitationsTitle: Digital Twins in Clinical Neurology: From Concept to Clinical Application in Multiple Sclerosis.
peer reviewedPublication types: Journal Article; Review
AI assessment, not yet reviewed by a person · version 1 · Community votes are separate from evidence review.