New computational tools advanced by two research teams promise to improve diagnostic classification and single-cell analysis workflows. A multi-institution validation showed AI models can classify urothelial neoplasms in digital pathology with clinical-grade performance, while CoDA-hd introduced compositional data modeling tailored for high-dimensional single-cell RNA-seq. The digital pathology work (authors Park, Kim et al.) supports deploying AI classifiers as decision-support tools that can reduce interobserver variability and speed diagnosis. CoDA-hd addresses compositional constraints in scRNA-seq data, improving downstream differential expression and cell-type analyses. Both contributions tackle bottlenecks in translational genomics: robust pattern recognition in clinical images and statistically sound modeling of sparse, compositional single-cell datasets. Adoption will depend on software portability, benchmarking, and regulatory acceptance for diagnostic use.