Identify medical terms in text
Predict chest diseases from X-ray images
Answer medical questions using ClinicalBERT
Analyze X-ray images to classify pneumonia types
Submit medical data to generate retinal case recommendations
Classify and assess severity of lung conditions from chest X-rays
Analyze retinal images to determine diabetic retinopathy severity
Segment medical images to identify gastrointestinal parts
Consult medical information with a chatbot
Classify medical images into six categories
Evaluate heart disease risk based on personal data
Evaluate cancer risk based on cell measurements
Predict sperm retrieval success rate
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.