Regardless of industry, machine learning and artificial intelligence are becoming integral components of business processes. But, these models must be trained well to get better diagnoses and improve patient care. This article has some key insights on why to use image annotation for healthcare AI.
The Key Takeaway from the Article is
- Whether managing health records or offering virtual assistance, the healthcare industry has evolved from a manual process to an automated one to reduce manual intervention and make health monitoring more accessible and better. But, now healthcare AI is moving beyond monitoring.
- Moreover, training these models requires high-quality data and images to get better data labeling for detection, classification, segmentation, and transcription. At this juncture, image annotation is a great help. Medical Image Annotation feeds the entire AI model with marked and labeled images and offers better predictive maintenance.
- Medical image annotation uses multiple techniques like bounding boxes landmarking, polygons, and other. In healthcare medical image annotation can help in detecting blood clotting, dental analysis, cancer cell identification, rental images analysis, detecting liver-specific ailments, improving documentation, and many other healthcare processes.
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