Identify medical terms in text
Submit medical data to generate retinal case recommendations
Predict chest diseases from X-ray images
Classify medical images into six categories
Mammography Images Classification
Search and encode medical terms into MedDRA
Identify diabetic retinopathy stages from retinal images
Upload an image and get a skin lesion prediction
Consult symptoms and reports with AI doctor
Evaluate your diabetes risk with input data
Generate detailed chest X-ray segmentations
Detect bone fractures from X-ray images
Detect tumors in brain images
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.