The document explains how AI and machine learning are transforming medical imaging by improving accuracy, speeding diagnosis, and reducing clinical workload. With medical images making up 90% of healthcare data yet largely unused, AI helps extract critical insights across modalities like CT, MRI, and X-ray. A case study with Johns Hopkins shows AI-driven brain hemorrhage segmentation achieving 93% accuracy and reducing scan review time from hours to seconds. Other applications include cancer screening, neurological disorder detection, and dental imaging. The paper concludes that rising imaging demand and shrinking radiologist supply make AI essential for future diagnostics.

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