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Dermatology and Skin Cancer Classification
In dermatology, computer vision algorithms are trained on thousands of dermoscopic and clinical images of skin lesions to detect and classify skin cancers. These systems help distinguish malignant conditions, such as melanoma, from benign moles or other skin abnormalities with accuracy comparable to, or sometimes exceeding, that of experienced dermatologists. AI-powered tools also aid in early detection, triaging suspicious cases for further examination, and supporting teledermatology applications where specialist access may be limited.
Neurology and Stroke Detection
Neurology is another case where AI systems perform best. These intelligent tools analyze CT and MRI scans in real time to detect signs of stroke, such as blocked or bleeding blood vessels. They can quickly flag critical cases and alert specialists, significantly reducing the time between diagnosis and treatment. Through early detection automation, AI will enhance patient outcomes, reduce brain damage, and increase survival rates. Additionally, continuous learning from large datasets allows these models to become even more accurate and reliable over time.
