Validation of generative artificial intelligence techniques for synthetic data generation in multiple sclerosis research: A comparison with real-world evidence from the Italian multiple sclerosis registry

BACKGROUND: The use of generative artificial intelligence to create synthetic data is an emerging strategy to enhance real-world evidence. OBJECTIVE: To assess the validity of artificial intelligence-generated synthetic data in replicating data collected in the Italian Multiple Sclerosis and Related Disorders Register for the evaluation of progression independent of relapse activity risk under early intensive treatment and escalation treatment strategies. METHODS: Artificial intelligence generative models were trained on a sub-cohort of 1666 Italian Multiple Sclerosis and Related Disorders Register patients to generate a synthetic dataset of 4878 patients. Synthetic vAlidation FramEwork, Clinical Synthetic Fidelity, and Nearest Neighbor Distance Ratio were applied. Comparisons between treatment strategies were conducted using both real and synthetic data, focusing on progression independent of relapse activity risk. RESULTS: The synthetic data demonstrated fidelity and privacy preservation. Treatment-effect estimates for escalation treatment versus early intensive treatment were similar across the real and synthetic datasets. Cox proportional hazards models showed that synthetic data preserved the robustness of the estimates for the risk of a first progression independent of relapse activity event. Associations with radiological risk factors were broadly similar across the real and synthetic datasets. CONCLUSION: Artificial intelligence-generated synthetic data reliably replicated treatment-effect outcomes of Italian Multiple Sclerosis and Related Disorders Register data, supporting their potential use in clinically defined research settings.

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Publication Details

Journal
Multiple Sclerosis Journal
Published
2026-10-14
DOI
https://doi.org/10.1177/13524585261492525
Primary Topic
Multiple Sclerosis Research Studies
Type
article
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article

Validation of generative artificial intelligence techniques for synthetic data generation in multiple sclerosis research: A comparison with real-world evidence from the Italian multiple sclerosis registry

Massimo Filippi, Diana Ferraro, Elisabetta Sauta, Saverio D’Amico et al.
Multiple Sclerosis Journal
Multiple Sclerosis Research Studies
article

Validation of generative artificial intelligence techniques for synthetic data generation in multiple sclerosis research: A comparison with real-world evidence from the Italian multiple sclerosis registry

Massimo Filippi, Diana Ferraro, Elisabetta Sauta, Saverio D’Amico, Maria Gabriella Coniglio, Victor Savevski, María Trojano, Tommaso Guerra, Emilio Portaccio, Maria Pia Amato, Pietro Iaffaldano, Giovanna De Luca, Francesca Caputo, Rocco Totaro, Clara Grazia Chisari, Damiano Paolicelli, Matilde Inglese, Matteo Foschi, Giuseppe Salemi, Massimiliano Copetti, Maria Assunta Rocca, Matteo Giovanni Della Porta, Mattia Delleani, Francesco Paolo Patti, Vincenzo Brescia Morra, Mario Alberto Battaglia, Giuseppe Lucisano, Filippo Emanuele Colella
article en

Abstract

BACKGROUND: The use of generative artificial intelligence to create synthetic data is an emerging strategy to enhance real-world evidence. OBJECTIVE: To assess the validity of artificial intelligence-generated synthetic data in replicating data collected in the Italian Multiple Sclerosis and Related Disorders Register for the evaluation of progression independent of relapse activity risk under early intensive treatment and escalation treatment strategies. METHODS: Artificial intelligence generative models were trained on a sub-cohort of 1666 Italian Multiple Sclerosis and Related Disorders Register patients to generate a synthetic dataset of 4878 patients. Synthetic vAlidation FramEwork, Clinical Synthetic Fidelity, and Nearest Neighbor Distance Ratio were applied. Comparisons between treatment strategies were conducted using both real and synthetic data, focusing on progression independent of relapse activity risk. RESULTS: The synthetic data demonstrated fidelity and privacy preservation. Treatment-effect estimates for escalation treatment versus early intensive treatment were similar across the real and synthetic datasets. Cox proportional hazards models showed that synthetic data preserved the robustness of the estimates for the risk of a first progression independent of relapse activity event. Associations with radiological risk factors were broadly similar across the real and synthetic datasets. CONCLUSION: Artificial intelligence-generated synthetic data reliably replicated treatment-effect outcomes of Italian Multiple Sclerosis and Related Disorders Register data, supporting their potential use in clinically defined research settings.

Multiple Sclerosis Journal
Humanitas University (IT), Vita-Salute San Raffaele University (IT), University of L'Aquila (IT), Casa Sollievo della Sofferenza (IT), University of Catania (IT), University of Chieti-Pescara (IT), Azienda Unita' Sanitaria Locale Di Modena (IT), Ospedale "Santa Maria delle Croci" di Ravenna (IT), Azienda Unità Sanitaria Locale Della Romagna (IT), Don Carlo Gnocchi Foundation (IT), Federico II University Hospital (IT), Ospedale Madonna Delle Grazie (IT), Policlinico Universitario di Catania (IT), IRCCS Ospedale San Raffaele (IT), Tecnologie Avanzate (Italy) (IT), Ospedale Policlinico San Martino (IT), Ospedale SS. Annunziata (IT), Center for Outcomes Research and Clinical Epidemiology (IT), IRCCS Humanitas Research Hospital (IT), Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele (IT), Fondazione Italiana Sclerosi Multipla (IT), University of Florence (IT), University of Bari Aldo Moro (IT), University of Palermo (IT)
Good health and well-being
Openalex Percentile: Top 13%
Multiple Sclerosis Research Studies
DOISourceOpenAlex
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