Back to search

Article

Validation of Generative AI Techniques for Synthetic Data Generation in Multiple Sclerosis Research: A Comparison with Real-World Evidence from the Italian MS Registry

2025-11-15

Abstract excerpt

<h4>Importance</h4> Large multiple sclerosis (MS) registries provide crucial real-world evidence but often suffer from missing data, inconsistencies, and privacy limitations that restrict data sharing. The use of generative AI to create synthetic data (SD) is an emerging strategy to enhance real-world evidence research potentially overcoming these challenges. <h4>Objective</h4> To evaluate the validity of AI-gen...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
19065620-740b-5bd7-9fa4-0c54590a2289
DOI
10.1101/2025.11.13.25340076
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Validation of Generative AI Techniques for Synthetic Data Generation in Multiple Sclerosis Research: A Comparison with Real-World Evidence from the Italian MS RegistryDOI 10.1101/2025.11.13.25340076
Select a neighboring publication to make it the new centre.