Training models on radiological images is still slowed by a basic infrastructure problem. Public datasets differ in file formats, directory structures, label schemas, and annotation types. Integrating them into a single training pipeline usually requires custom, one-off engineering for each source.

RadHarmony is an open-source library designed to remove that friction. It standardizes metadata from 24 public datasets into a unified format and delivers samples through a single interface that supports classification labels, segmentation masks, bounding boxes, and radiology reports. The system is built to work with chest radiographs first, with early support for CT and MRI.