OpenRad or Open Chaos? Cleaning Up Radiology AI’s Model Mess
Models are easy to announce. They are harder to find, harder to reuse, and much harder to trust. That is the uncomfortable starting point for radiology AI. The field is not suffering from a shortage of algorithms. It has models for lesion detection, segmentation, image reconstruction, report generation, modality-specific classification, and increasingly fashionable foundation-style systems. The difficulty begins one step later, when someone asks a boring but lethal operational question: Where is the model, what does it actually do, and can we use it without conducting an archaeological expedition through GitHub, supplementary PDFs, broken links, and optimistic abstracts? ...