Summary
Multimodal datasets, which combine sources such as medical imaging, clinical records, and genetic information, have the potential to significantly advance our understanding of complex systems and improve health outcomes. However, the heterogeneity, high dimensionality, and lack of reliable statistical tools often lead to unstable analyses or misleading conclusions. These issues — and the limited ability to rigorously quantify uncertainty or disentangle relationships among data sources — pose a major barrier to the adoption of data-driven methods in high-stakes settings, where the cost of error
