arXiv:2607.00127v1 Announce Type: new Abstract: Survival analysis models time-to-event data, but in clinical settings training data are costly and scarce: events accrue over years of follow-up, cohorts are small, and privacy regulations restrict sharing across institutions. Tabular generative models promise augmentation and privacy-preserving cohort sharing, yet are themselves data-hungry -- on the small cohorts typical of survival analysis, a single generator rarely characterizes the population well enough for downstream models trained on its output to match real-data performance. FoGS (Filte
The proliferation of generative AI models and the increasing demand for high-quality, privacy-preserving synthetic data coincides with the long-standing challenge of data scarcity in specialized fields like clinical survival analysis.
This development could unlock new possibilities for AI model training in highly sensitive and data-scarce domains, accelerating research and development where real data is impractical to acquire or share.
The ability to generate high-fidelity synthetic data even from small, complex real datasets shifts the bottleneck from data acquisition to the sophistication of generative models themselves, particularly for time-to-event analysis.
- · Clinical research institutions
- · Generative AI startups
- · Healthcare AI developers
- · Patients (through faster drug development)
- · Data brokers (for certain verticals)
- · Traditional statistical methods (in some applications)
Improved performance and robustness of survival analysis models due to augmented training data.
Accelerated discovery of new treatments and predictive biomarkers in medical fields by making AI more accessible.
Potential for new ethical and regulatory frameworks around the use and validity of synthetic medical data for clinical decision-making.
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Read at arXiv cs.LG