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Why Radiology AI Didn’t Work and What Comes Next
FDA Approvals, PACS Pain, Selling Point Solutions, and Why Radiology AI Needed a Rethink
Clinical documentation, especially in radiology, is filled with hedge language — phrases like “cannot rule out,” “may represent,” or “follow-up recommended for correlation.” These aren’t careless ambiguities; they’re defensive signals, shaped by decades of legal precedent and diagnostic uncertainty. Ask yourself: if that patient presented to a hospital today, would an AI model trained on conventional trauma datasets flag “ice pick injury to the brain”? Radiology AI tools were frequently priced as standalone software products, even though they were delivering value by improving clinical throughput, turnaround times, and operational efficiency.
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