Synthetische data helpen biomedische AI-modellen, maar kloof met werkelijkheid blijft

Synthetic datasets are increasingly vital for training biomedical machine learning systems, yet researchers face a significant challenge: models performing well on simulated data often underperform with real-world information. A new analysis examines this simulation-to-reality gap and its implications for developing reliable AI tools in healthcare applications.