Annotation for Healthcare AI & Medical Data Extraction Services
Need of data annotation in healthcare and medical industry
Healthcare industry features most of the existing data as unstructured without digitalization. The development of superior AI-based healthcare applications such as medical data extraction tools, autonomous robot assisted surgery, digital pathology reports, and healthcare chatbot requires excellent data transcription, identification, and annotation to train the computer vision deep learning models.
How is data labeling used for healthcare & medical industry?
NLP to document prescriptions, journals, medical books
Semantic segmentation for AI radiology, x-rays and digital pathology
Point annotation to label smaller objects in medical reports
Text extraction methods using advanced medical lexicon to train AI assistants and chatbots
Polygon annotation to assist in training data for robotic surgery and diagnosis
Panoptic segmentation for medical image annotation
Annotation tools used in medical & healthcare industry
With our deep understanding of healthcare terminologies and annotation capabilities, we take a flexible approach to work closely with you.
Our beliefs behind each annotations
Data Security & Privacy
Fast Delivery with High Accuracy
Cost Effective Pricing
Scalable Solution by Experts
A medical annotator is an image or video annotator specializing in labeling or annotating medical-related images and videos. A medical annotator is involved in annotating medical imaging data, including CT, MRI scans, Ultrasound, and X-rays. Medical annotators help in the technological development of AI algorithms used in the medical field to improve patient-doctor interactions.
Artificial intelligence is revolutionizing processes in the field of medicine. AI is extensively fielded to diagnose patients, is used in the research and development of drugs, and is used to transcribe medical documents for ease of retrieval. It is used to improve communication between patients and physicians. In recent years, AI has been used to treat patients remotely.
Electronic Health Records (EHR) allow healthcare providers to collect, retrieve and report health data. The process of extracting health data from EHR is straightforward, involving retrieving and extracting data from data records such as prescriptions, lab results, etc. However, the data extracted is specific to the procedures carried out by each healthcare provider.
The first process in Electronic Medical Records (EMR) data extraction is to export all available data to the new system in a usable format. The text notes are processed using natural language processing (NLP) tools. AI is also used to extract unstructured data to derive value. API specifically developed for medical data extraction can also be used to integrate with EMR and extract valuable data.