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How is AI used in medical imaging and diagnostics?

AI revolutionizes medical imaging through pattern recognition exceeding human capabilities. Radiology AI analyzes X-rays, CT scans, MRIs, and ultrasounds to detect abnormalities: fractures, tumors, hemorrhages, pneumonia, pulmonary embolisms, and more. Deep learning models trained on millions of images achieve 85-95% sensitivity and specificity, matching or exceeding radiologist performance in specific tasks. Key applications include: screening prioritization (flagging urgent cases for immediate review), automated measurements (tumor size tracking, cardiac function assessment), comparison studies (highlighting changes from prior imaging), and anomaly detection (catching subtle findings that might be overlooked). Pathology AI analyzes digitized tissue slides to identify cancerous cells, grade tumors, predict treatment response, and quantify biomarkers. Computer vision processes gigapixel images faster and more consistently than human pathologists examining slides under microscopes. Ophthalmology AI detects diabetic retinopathy, age-related macular degeneration, and glaucoma from retinal images with 90%+ accuracy, enabling automated screening in primary care settings. Dermatology AI classifies skin lesions, distinguishing benign from malignant with expert-level accuracy from smartphone photos. Clinical impact includes faster turnaround times (results in minutes vs. hours), reduced variability between readers, workload reduction for overwhelmed specialists, and democratized access to expert-level diagnostics in areas lacking specialists.