General Text Annotation
Cognitive text annotation and labeling are services that enable organizations to find critical information in unstructured texts. Machines can understand human language by annotating text. You can also read about the advanced data annotation services online.
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Annotation for Medical Text
Unstructured data makes up 80% of healthcare data, which is why it is not accessible to traditional analytics tools. It limits the amount of data that can be used and its impact on decision-making in an organization.
Unstructured data could include physician notes, discharge summaries, and pathology reports. Natural language processing is used to provide domain-specific insights on information such as symptoms, diseases, medication, and medication to drive care insights.
- Scale-up as you need – pay-as-you-grow business model
- This platform can be used to annotate with PHI in Mind
- Extracting concepts from unstructured text in medical records de-identified
- An annotation platform that is highly customizable, allowing you to customize labels for different healthcare uses.
General Image Annotation
Image annotation refers to the act of associating an image with an identifier. To create training data sets for your machine learning models, you can use bounding boxes, 3D cuboids, and semantic segmentation.
AI-enabled systems that combine human annotators with AI can increase the efficiency of automating repetitive tasks that are most susceptible to errors.
Annotation for Medical Images
Sharp understands the importance of medical imagery for healthcare. Medical image annotation is crucial for everything, from identifying anomalies and tumors that might go unnoticed by the human eye to studying carcinogens or diseases. It requires a high level of industry knowledge and skill.
AI-backed machines use computer visualization to detect patterns and to correlate them with medical imaging data.