Don’t be data poor — Anuj Iravane, Anterior

Synthetic data generation offers a viable solution for healthcare AI development when real-world medical records are restricted by strict PHI privacy regulations. By reversing the standard inference workflow—sampling labels and reasoning traces before generating data—teams can create diverse, high-fidelity synthetic datasets that model complex clinical trajectories. This process utilizes symbolic policy representations to ensure accuracy and consistency, while a course-to-fine layering approach allows for efficient, scalable document generation. Empowering domain experts to steer this pipeline through human-in-the-loop mechanisms and skill-based workflows ensures the generated data remains clinically relevant and useful for testing edge cases. Ultimately, this approach enables organizations to maintain high production accuracy and perform rigorous evaluations without relying on the retention of sensitive, real-world patient data.