Identify medical terms in text
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Predict sepsis based on patient data
Answer medical questions using ClinicalBERT
Ask questions to get AI medical diagnostics
Classify MRI images to detect brain tumors
Display medical image predictions and metrics
Answer medical questions using real-time AI
Predict diabetes risk based on medical data
Consult medical information with a chatbot
Classify lung cancer cases from images
Analyze OCT images to diagnose retinal conditions
Segment 3D medical images with text and spatial prompts
Clinical AI Apollo Medical NER is a specialized Named Entity Recognition (NER) tool designed for the medical domain. It is engineered to identify and extract medical entities such as diseases, symptoms, medications, and procedures from unstructured text data. This solution is particularly useful for processing clinical notes, medical reports, and other healthcare-related documents, enabling efficient data analysis and decision-making.
What types of entities can Apollo Medical NER identify?
Apollo Medical NER can identify a wide range of medical entities, including diseases, symptoms, medications, procedures, anatomical terms, and lab tests. Customization options allow you to expand this list based on your specific needs.
Is Apollo Medical NER suitable for real-time applications?
Yes, Apollo Medical NER is designed to process text data quickly, making it suitable for real-time applications such as emergency room notes or live patient consultations.
How accurate is Apollo Medical NER?
Apollo Medical NER achieves high accuracy in medical entity recognition, with precision and recall rates that exceed many industry standards. Accuracy can be further improved by fine-tuning the model for your specific use case.