A crisp 101 on synthetic imaging and how generated datasets power model development and evaluation.
Real-world data is messy, biased, and hard to scale. Use synthetic imaging to close coverage gaps and improve robustness.
Expose hidden weaknesses before deployment using targeted synthetic datasets and audits.
Why regulators care about coverage, bias control, and traceability — and where synthetic data fits.
FDA & EMA case studies: Grand Rounds, VICTRE, M-SYNTH, and rare disease applications.
Developer playbook: traceability, bias audits, packaging synthetic + real for submissions.
When dimension matters most—tradeoffs for speed, memory, and outcomes.
Use synthetic datasets to accelerate testing, validation, and documentation.
