Scalable and Privacy-Conscious End-to-End Processing of Large-Scale Clinical Data for Precision Medicine: Empirical Evaluation Study

By significantly mitigating data processing bottlenecks, a Parquet-based pipeline enabled high-throughput, large-scale clinical evidence generation without compromising model integrity or patient privacy. This framework provides a scalable and robust infrastructure for precision medicine, facilitating agile multicenter collaborations and real-world data analysis in resource-constrained clinical environments.