Radiology AI

Chest X-rays are not product reviews. This should not need saying, but much of automated report evaluation has behaved as if the difference were mostly decorative. A generated radiology report can sound fluent, mention familiar anatomy, and overlap nicely with a reference report while still missing the sentence that matters. A model that overlooks a life-threatening pneumothorax has not made the same kind of mistake as a model that fails to mention age-appropriate aortic calcification. One error can change patient management immediately. The other may be little more than reporting style. ...

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? ...