- SYN-Y2-2025-017: Publication [Iascone et al., IRCCS Humanitas Research Hospital]
- Blood, journal of the American Society of Hematology, November 2025. Conference: ASH2025. Read here >
- The paper introduces SAFE (Synthetic vAlidation FramEwork), a comprehensive system for evaluating multimodal Synthetic Data (SD) in terms of statistical fidelity, clinical utility, and privacy. Motivated by the growing use of Generative AI in applications such as digital twins and synthetic control arms—and the lack of standardized validation tools—the authors focus on hematology, where large-scale multimodal data are critical for advancing personalized care in Myeloid Neoplasms (MN). SAFE is applied to SD generated from the large TITAN cohort (n=20,054), covering clinical, genomic, transcriptomic, and histopathological image data. Developed within the SYNTHEMA and SYNTHIA consortia, the framework includes modular analyses for tabular, longitudinal, and imaging data, along with an RNA-seq validation pipeline and clinically driven evaluation using the MOSAIC framework. The synthetic cohort closely reflects real disease stratification, achieving high fidelity and utility scores (CSF 91%, GSF 88%, CSU 90.2%, TSF 88%, PSS 86%) and an overall SAFE score of 89%. Synthetic data performed comparably to real data in patient stratification, prognostic scoring, survival analysis, and feature distributions, while maintaining low re-identification risk. The authors conclude that SAFE offers a robust, disease-specific approach to validating SD and can support trustworthy clinical research and future regulatory adoption of AI-generated evidence in hematology.
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