Abstract
In hematology, leveraging real-world multimodal data at large scale is crucial for developing personalized medicine to address unmet clinical needs, particularly for rare diseases. Generative AI in healthcare shows great promise by generating multimodal synthetic data (SD) to improve patients’ diagnosis and prognosis while accelerating clinical research (PMID: 34131324). The challenges in generating SD include accessing complete real-world datasets for model training, maintaining intrinsic relationships among different data layers, and ensuring clinical accuracy and privacy protection.
This project conducted by GenoMed4All and Synthema consortia, aimed to: 1) implement an innovative approach for generating high-fidelity multimodal SD from patients with myeloid neoplasms (MN); 2) develop a comprehensive multimodal Synthetic Validation Framework (SVF) to assess the SD clinical and statistical fidelity and privacy preservation; 3) verify the SD technology capability to accelerate research and enhance predictive models through multimodal data integration.