Multiple sclerosis (Houndmills, Basingstoke, England) · 2026
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.
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Iaffaldano Pietro, D'Amico Saverio, Lucisano Giuseppe, Copetti Massimiliano, Guerra Tommaso, Rocca Maria A, Patti Francesco, De Luca Giovanna, Ferraro Diana, Totaro Rocco, Morra Vincenzo Brescia, Salemi Giuseppe, Portaccio Emilio, Foschi Matteo, Inglese Matilde, Coniglio Maria G, Chisari Clara G, Caputo Francesca, Paolicelli Damiano, Battaglia Mario A, Della Porta Matteo G, Savevski Victor, Delleani Mattia, Colella Filippo E, Sauta Elisabetta, Amato Maria P, Filippi Massimo, Trojano Maria
Аннотация
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.
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.