Data Annotation for Healthcare AI

Human-Powered Medical Data Annotation

Unlock complex information in unstructured data with entity extraction and recognition

Medical Data Annotation

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Empowering teams to build world-leading AI products.

There’s an increasing demand to analyze unstructured, complex medical data to uncover undiscovered insights

80% of data in the healthcare domain is unstructured, making it inaccessible. Accessing the data requires significant manual intervention, which limits the quantity of usable data. Understanding text in the medical domain requires a deep understanding of its terminology to unlock its potential. Shaip provides the expertise to annotate healthcare data to improve AI engines at scale.

IDC, Analyst Firm:

The worldwide installed base of storage capacity will reach 11.7 zettabytes in 2023

IBM, Gartner & IDC:

80% of the data around the world is unstructured, making it obsolete and unusable. 

Real-World Solution

Analyze data to discover meaningful insights to train NLP models with Medical Text Data Annotation

We offer Medical Data annotation services that help organizations extract critical information in unstructured medical data, i.e., Physician notes, EHR admission/discharge summaries, pathology reports, etc., that help machines to identify the clinical entities present in a given text or image. Our credentialed domain experts can help you deliver domain-specific insights – i.e., symptoms, disease, allergies, & medication, to help drive insights for care.

Real-World Solution

We also offer proprietary Medical NER APIs (pre-trained NLP models), which can auto-identify & classify the named entities presented in a text document. Medical NER APIs leverage proprietary knowledge graph, with 20M+ relationships & 1.7M+ clinical concepts

From data licensing, and collection, to data annotation, Shaip has got you covered.

  • Annotation and preparation of medical images, videos, and texts, including radiography, ultrasound, mammography, CT scans, MRIs, and photon emission tomography
  • Pharmaceutical and other healthcare use cases for natural language processing (NLP), including medical text categorization, named entity identification, text analysis, etc.

Medical Annotation Process

Annotation process generally differs to a client’s requirement but it majorly involves:

Domain Expertise

Phase 1: Technical domain expertise (Understand scope & annotation guidelines)

Training Resources

Phase 2: Training appropriate resources for the project

Qa Documents

Phase 3: Feedback cycle and QA of the annotated documents

Our Expertise

1. Clinical Entity Recognition/Annotation

A large amount of medical data and knowledge is available in the medical records mainly in an unstructured format. Medical entity Annotation enables us to convert unstructured data into a structured format.

Clinical Entity Annotation
Medicine Attributes

2. Attribution Annotation

2.1 Medicine Attributes

Medications and their attributes are documented in almost every medical record, which is an important part of the clinical domain. We can identify and annotate the various attributes of medications according to guidelines.

2.2 Lab Data Attributes

Lab data is mostly accompanied by their attributes in a medical record. We can identify and annotate the various attributes of lab data according to guidelines.

Lab Data Attributes
Body Measurement Attributes

2.3 Body Measurement Attributes

Body measurement is mostly accompanied by their attributes in a medical record. It mostly comprises of the vital signs. We can identify and annotate the various attributes of body measurement.

3. Oncology Specific NER Annotation

Along with generic medical NER annotation, we can also work on domain specific annotations like oncology, radiology, etc. Here are the oncology specific NER entities that can be anotated – Cancer problem, Histology, Cancer stage, TNM stage, Cancer grade, Dimension, Clinical status, Tumor marker test, Cancer medicine, Cancer surgery, Radiation, Gene studied, Variation code, Body site

Oncology Specific Ner Annotation
Adverse Effect Annotation

4. Adverse Effect NER & Relationship Annotation

Along with identifying and annotating major clinical entities and relationships, we can also annotate the adverse effects of certain drugs or procedures. The scope is as follows: Labeling adverse effects and their causative agents. Assigning the relationship between the adverse effect and the cause of the effect.

5. Relationship Annotation

After identifying and annotating clinical entities, we also assign relevant relationship among the entities. Relationships may exist between two or more concepts.

Relationship Annotation

6. Assertion Annotation

Along with identifying clinical entities and relationships, we can also assign the Status, Negation and Subject of the clinical entities.


7. Temporal Annotation

Annotating temporal entities from a medical record, helps in building a timeline of the patient’s journey. It provides reference and context to the date associated with a specific event. Here are the date entities – Diagnosis date, Procedure date, Medication start date, Medication end date, Radiation start date, Radiation end date, Date of admission, Date of discharge, Date of consultation, Note date, Onset.

Temporal Annotation
Section Annotation

8. Section Annotation

It refers to the process of systematically organizing, labeling, and categorizing different sections or parts of healthcare-related documents, images, or data i.e., annotation of relevant sections from the document and classification of the sections into their respective types. This helps in creating structured and easily accessible information, which can be used for various purposes such as clinical decision support, medical research, and healthcare data analysis.

9. ICD-10-CM & CPT Coding

Annotation of ICD-10-CM and CPT codes according to the guidelines. For each labeled medical code, the evidence (text snippets) that substantiate the labeling decision will be also annotated along with the code.

Icd-10-Cm &Amp; Cpt Coding
Rxnorm Coding

10. RXNORM Coding

Annotation of RXNORM codes according to the guidelines. For each labeled medical code, the evidence (text snippets) that substantiate the labeling decision will be also annotated along with the code.0

11. SNOMED Coding

Annotation of SNOMED codes according to the guidelines. For each labeled medical code, the evidence (text snippets) that substantiate the labeling decision will be also annotated along with the code.

Snomed Coding
Umls Coding

12. UMLS Coding

Annotation of UMLS codes according to the guidelines. For each labeled medical code, the evidence (text snippets) that substantiate the labeling decision will be also annotated along with the code.

Reasons to choose Shaip as your trustworthy Medical Annotation Partner



Dedicated and trained teams:

  • 30,000+ collaborators for Data Creation, Labeling & QA
  • Credentialed Project Management Team
  • Experienced Product Development Team
  • Talent Pool Sourcing & Onboarding Team


Highest process efficiency is assured with:

  • Robust 6 Sigma Stage-Gate Process
  • A dedicated team of 6 Sigma black belts – Key process owners & Quality compliance
  • Continuous Improvement & Feedback Loop


The patented platform offers benefits:

  • Web-based end-to-end platform
  • Impeccable Quality
  • Faster TAT
  • Seamless Delivery
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Named Entity Recognition is a part of Natural Language Processing. The primary objective of NER is to process structured and unstructured data and classify these named entities into predefined categories. Some common categories include name, location, company, time, monetary values, events, and more.

In a nutshell, NER deals with:

Named entity recognition/detection – Identifying a word or series of words in a document.

Named entity classification – Classifying every detected entity into predefined categories.

Natural Language processing helps develop intelligent machines capable of extracting meaning from speech and text. Machine Learning helps these intelligent systems continue learning by training on large amounts of natural language data sets. Generally, NLP consists of three major categories:

Understanding the structure and rules of the language – Syntax

Deriving the meaning of words, text, and speech and identifying their relationships – Semantics

Identifying and recognizing spoken words and transforming them into text – Speech

Some of the common examples of a predetermined entity categorization are:

Person: Michael Jackson, Oprah Winfrey, Barack Obama, Susan Sarandon

Location: Canada, Honolulu, Bangkok, Brazil, Cambridge

Organization: Samsung, Disney, Yale University, Google

Time: 15.35, 12 PM,

The different approaches to creating NER systems are:

Dictionary-based systems

Rule-based systems

Machine learning-based systems

Streamlined Customer Support

Efficient Human Resources

Simplified Content Classification

Optimizing Search Engines

Accurate Content recommendation