Editing Radiology AI (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Radiology AI operates at the intersection of computer vision, clinical medicine, and regulatory compliance. Unlike general image recognition, radiology AI must be FDA-cleared, validated prospectively, and integrated into clinical workflows — a multi-year process beyond model development.

**The CheXNet moment (2017)**: Stanford's CheXNet paper shocked the field by demonstrating a neural network achieving expert-level pneumonia detection on chest X-rays. Trained on 100,000 chest X-rays with radiologist labels, DenseNet-121 matched or exceeded the average radiologist on pneumonia detection. This triggered massive investment in radiology AI.

**Commercial deployment landscape**: Multiple AI vendors now offer FDA-cleared radiology AI: Viz.ai for PE and stroke, Aidoc for critical finding detection, Enlitic for chest X-ray, iCAD for mammography, Zebra Medical Vision for bone density. These tools are integrated into PACS workflows, running automatically on incoming studies and flagging critical findings for priority read.

**The workflow integration challenge**: AI models that work in research fail in clinical deployment due to: scanner vendor differences (GE vs. Siemens CT reconstruction filters), unexpected patient populations, edge cases not in training, and PACS integration complexity. Prospective clinical validation in the target deployment environment is essential.

**AI triage**: The highest-value radiology AI use case: automatically detecting critical, time-sensitive findings (intracranial hemorrhage, PE, pneumothorax, aortic dissection) and routing those studies to the top of the reading queue. This reduces time-to-treatment without requiring AI to replace the radiologist — a compelling value proposition that has driven commercial adoption.
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