Healthcare Annotation

Named Entity Recognition

Medical Annotation Services

 
Our Medical Annotation services empower AI accuracy in healthcare by meticulously labeling medical images, texts, and audio. With our expertise, we train AI models that significantly improve diagnostics, treatment planning, and patient care. We ensure high-quality, reliable data for advanced medical technology applications, helping you enhance your AI’s medical proficiency. Trust us to support your journey toward better healthcare outcomes.

Image Annotation

Boost medical AI by accurately annotating visual data from X-rays, CT scans, and MRIs, ensuring that AI models excel in diagnostics and treatment planning. Expert-driven data labeling enhances the precision of these models, ultimately improving patient outcomes through more reliable and insightful imaging interpretations.

Video Annotation

Elevate healthcare AI by implementing detailed video annotation, optimizing AI learning through precise classifications and segmentations in medical footage. This approach enhances the performance of AI systems in surgical assistance and patient monitoring, driving superior diagnostics and overall healthcare outcomes.

Text Annotation

Accelerate medical AI development with expertly annotated text data, enabling swift parsing and enrichment of large volumes, from handwritten notes to insurance reports. This ensures accurate, actionable insights that drive advancements in healthcare and improve decision-making.

Audio Annotation


Effectively leverage NLP expertise to annotate and label medical audio data with precision, enabling the creation of voice-assisted systems that streamline clinical workflows. Integrate AI into voice-activated healthcare solutions to enhance diagnostic accuracy and improve overall patient care through expertly curated audio data.

Medical Coding


Streamline medical documentation with AI-driven medical coding, converting records into universal codes for enhanced accuracy and billing efficiency, while supporting seamless healthcare delivery through advanced AI assistance in record management.

NLP Annotation


Apply natural language processing to annotate unstructured medical data, including medical literature and EHRs, for AI systems that extract insights, detect trends, and predict patient outcomes.

Our Expertise

Named Entity Recognition (NER)

 
Annotation in the medical context involves identifying and classifying key entities such as diseases, medications, symptoms, and procedures within text. It includes linking these entities to standardized databases, annotating relationships between them, and marking temporal information and negations. This enhances AI understanding of medical records, improving diagnostics and treatment decisions through contextual insights.
 
 

Named Entity Recognition (NER-QC)

 
Annotation quality checks in healthcare involve expert reviews to ensure accuracy and relevance of labeled data. Consistency checks and inter-annotator agreement metrics assess reliability and uniformity across annotations. Implementing a feedback loop for annotators enhances their skills, while automated validation tools and data quality metrics help identify and rectify errors, leading to continuous improvement in the annotation process.

Relationship Annotation

Relationship annotation in healthcare involves identifying and labeling connections between various entities, such as linking diseases to their symptoms, medications, and procedures. This process enhances AI models’ contextual understanding of medical texts, improving insights into patient care and treatment plans. By structuring information, relationship annotations support clinical decision-making and predictive analytics, ultimately leading to better patient outcomes.

Relationship Annotation (QC)

 
Relationship Quality Assurance (QA) in healthcare focuses on evaluating the accuracy and reliability of relationship annotations between medical entities. It involves expert reviews to ensure that annotations reflect clinical knowledge and consistency checks to assess inter-annotator agreement. By implementing feedback mechanisms and training for annotators, along with automated validation tools, organizations can continuously improve the quality of relationship annotations.

Assertion Annotation

 

Assertion annotation in healthcare involves identifying and labeling the certainty, negation, or uncertainty of medical statements within clinical texts. This includes affirmative assertions (e.g., “Patient has diabetes”), negated assertions (e.g., “No signs of infection”), and uncertain assertions (e.g., “Possible fracture”). By providing clarity on the nuances of patient information, assertion annotations enhance diagnostic accuracy and clinical decision-making. Additionally, they improve AI models in natural language processing by helping systems accurately interpret clinical notes and patient records.

Assertion QC

Assertion Quality Control (QC) ensures the accuracy and consistency of assertion annotations in clinical texts, focusing on the clarity of certainty, negation, and uncertainty. This process involves expert reviews and consistency checks to align annotations with clinical standards. Feedback mechanisms and targeted training help improve annotators’ skills, while automated tools identify and correct common errors. Ultimately, this enhances the overall quality of medical annotations.