Data Annotation for Healthcare AI

Human-Powered Medical Data Annotation

Unlock complex information in unstructured data with entity extraction and recognition

Medical Ner

Featured Clients

Empowering teams to build world-leading AI products.

There’s an increasing demand to analyze unstructured, complex medical data to uncover undiscovered insights. Medical data annotation comes to the rescue

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 you 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.

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

Real-World Solution

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 (Understanding project 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. 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
Adverse Effect Annotation

4. Adverse effect 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. PHI De-identification

Our PHI/PII deidentification capabilities include removal of sensitive information such as names and social security numbers that may directly or indirectly connect an individual to their personal data. Its what patients deserve and HIPAA demands.

De-Identify Free Text Documents

6. Electronic Medical Records (EMRs)

Medical practitioners gain significant insight from Electronic Medical Records (EMRs) and physician clinical reports. Our experts can extract complex medical text that can be used in disease registries, clinical trials, and healthcare audits.

7. Status/Negation/Subject

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


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

Why Shaip?

Dedicate Team

It is estimated that data scientists spend over 80% of their time in data preparation. With outsourcing, your team can focus on the development of robust algorithms, leaving the tedious part of collecting the named entity recognition datasets to us.


An average ML model would require collection and tagging large chunks of named datasets, which requires companies to pull in resources from other teams. With partners like us, we offer domain experts which can be easily scaled as your business grows.

Better Quality

Dedicated domain experts, who annotate day-in and day-out will – any day – do a superior job when compared to a team, that needs to accommodate annotation tasks in their busy schedules. Needless to say, it results in better output.

Operational Excellence

Our proven data quality assurance process, technology validations, and multiple stages of QA, helps us deliver best-in-class quality that ofen exceeds expectations.

Security with Privacy

We are certified for maintaining the highest standards of data security with privacy while working with our clients to ensure confidentiality

Competitive Pricing

As experts in curating, training, and managing teams of skilled workers, we can ensure projects are delivered within budget.

Shaip Contact Us

Looking for Healthcare Annotation Experts for complex projects?

Contact us now to learn how we can collect and annotate dataset for your unique AI/ML solution

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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