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Unlock complex information in unstructured data with entity extraction and recognition
Medical data annotation is the process of labeling clinical text, medical images, audio, and video, using domain experts, so AI models can accurately recognize clinical entities, diagnoses, and anatomy. We offer HIPAA-compliant medical data annotation across radiology, oncology, cardiology, and clinical NLP, helping organizations extract critical information from unstructured data such as physician notes, EHR summaries, and pathology reports. Our credentialed domain experts deliver insights on symptoms, disease, allergies, and medication, backed by a proprietary knowledge graph of 20M+ relationships and 1.7M+ clinical concepts. From data licensing and collection to annotation, Shaip has you covered.
Our Medical Annotation services empower AI accuracy in healthcare. We meticulously label medical images, texts, and audio, using our expertise to train AI models. Our expert team, including medical experts and healthcare professionals, supervises and validates the annotation process to ensure clinical accuracy and compliance. These models improve diagnostics, treatment planning, and patient care. Ensure high-quality, reliable data for advanced medical technology applications. We understand the significant effort required to meet stringent quality and compliance standards in medical data annotation. Trust us to enhance your AI’s medical proficiency.
Enhance medical AI by annotating visual data from X-rays, CT scans, and MRIs. Medical image annotation and imaging annotation are specialized processes that involve expert-driven labeling of complex medical images to create high-quality datasets for healthcare AI systems.
Sharpen AI learning with classifications and segmentations in medical footage. Improve your surgical AI and patient monitoring for improved healthcare delivery and diagnostics. Annotated medical videos are essential for clinical applications, supporting real-world usecases.
Streamline medical AI development with expertly annotated text data, prepared by skilled medical annotators and data annotators. Quickly parse and enrich vast text volumes, from hand-written notes to insurance reports. Ensure accurate and actionable insights for healthcare advancements.
Streamline medical documentation by converting it into universal codes with AI medical coding, using data collected from various medical centers. Ensure accuracy, enhance billing efficiency, and support seamless healthcare service delivery with cutting-edge AI assistance in medical record coding.
Leverage NLP expertise to annotate and label medical audio data accurately, with medical professionals involved in annotation process. Craft voice-assisted systems for seamless clinical ops and integrate AI into voice-activated healthcare products. Enhance diagnostic precision with expert audio data curation.
In medical data annotation, the labeling process often utilizes specialized annotation tools, including DICOM viewers for basic image annotation tasks. While DICOM viewers are commonly used by radiologists for routine work, advanced annotation tools are essential for accurate and efficient labeling, especially when preparing data for machine learning and deep learning applications. Annotation process generally differs to a client’s requirement but it majorly involves:
Phase 1: Technical domain expertise (Understand scope & annotation guidelines)
Phase 2: Training appropriate resources for the project
Phase 3: Feedback cycle and QA of the annotated documents
Advanced AI and ML algorithms are transforming healthcare by utilizing various medical processes. Annotated data plays a crucial role in medical applications, supporting healthcare organizations in developing and training accurate healthcare AI models for diagnostics, disease identification, and anomaly detection. These cutting-edge technologies enable healthcare automation, leading to enhanced efficiency, precision, and patient care. To better understand their potential impact, let’s explore the following use cases:
Our radiology image annotation service sharpens AI diagnostics and includes an added layer of expertise. Each X-ray, MRI, and CT scan is meticulously labeled and reviewed by a subject matter expert. These annotated images serve as training data to train machine learning models for radiology diagnostics. This extra step in training and reviewing spots abnormalities and diseases.
Our cardiology-focused image annotation sharpens AI diagnostics. We bring in cardiology experts who label complex heart-related images and train our AI models. Before we send data to clients, these specialists review each image to ensure top-notch accuracy. This process empowers AI to detect heart conditions more precisely.
Our image annotation service in dentistry labels dental imagery, focusing on identifying various medical conditions, to enhance AI diagnostic tools. By accurately identifying tooth decay, alignment issues, and other dental conditions, our SMEs empower AI to improve patient outcomes and support dentists in precise treatment planning and early detection.
We put pathology SMEs on your whole-slide and biopsy images, segmenting cells, tissue regions, and abnormalities — with every slide reviewed before delivery. That expert layer gives your tumor-grading and disease-detection models ground truth they can trust.
Our radiology-trained SMEs delineate tumors and surrounding anatomy across CT, MRI, and PET, with precise segmentation and staging labels reviewed for accuracy. The result is training data your AI can rely on for early cancer detection and treatment planning.
We label fundus and OCT scans with ophthalmology-aware SMEs who mark subtle lesions and grade severity consistently. Clean, expert-reviewed annotations give your AI the ground truth it needs to flag diabetic retinopathy, glaucoma, and other eye disease early.
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.
After identifying and annotating clinical entities, we also assign relevant relationship among the entities. Relationships may exist between two or more concepts.
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.
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.
Along with generic medical NER annotation, we also work on domain specific annotations like oncology, radiology, etc. Oncology specific NER entities that can be annotated – Cancer problem, Histology, Cancer stage, TNM stage, Cancer grade, Dimension, Clinical status, Tumor marker test, Cancer medicine, Cancer surgery, Radiation, Gene studied, Variation code.
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.
Annotating temporal entities 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.
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.
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.
Our image annotation service specializes in CT scans for precise labeling for AI training with a keen focus on detailed anatomical structures. Subject matter experts not only review but also train on each image for top-notch accuracy. This meticulous process aids in the development of diagnostic tools.
Our MRI image annotation service fine-tunes AI diagnostics. Our subject matter experts train and review each scan for utmost precision before delivery. We label MRI scans accurately to enhance AI model training. This process helps them pinpoint anomalies and structures. Boost accuracy in medical assessments and treatment plans with our services.
X-ray image annotation sharpens AI diagnostics. Our experts label each image with care by pinpointing fractures and abnormalities accurately. They also train and review these labels for top accuracy before client delivery. Trust us to refine your AI and get better medical imaging analysis.
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.
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.
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.
Along with identifying clinical entities and relationships, we can also assign the Status, Negation and Subject of the clinical entities.
Clinical Insurance Annotation
The prior authorization process is key in connecting healthcare providers, payers and making sure treatments follow guidelines. Annotating medical records helped optimize this process. It matched documents to questions while following standards, improving client workflows.
Problem: Annotation of 6,000 medical cases had to be done within a strict timeline accurately, given healthcare data sensitivity. Strict adherence to updated clinical guidelines and privacy regulations like HIPAA was needed to ensure quality annotations and compliance, which is especially critical for clinical diagnostics to maintain dataset integrity and meet regulatory requirements.
Solution: We annotated over 6,000 medical cases, correlating medical documents with clinical questionnaires. This required meticulously linking evidence to responses while adhering to clinical guidelines. Key challenges addressed were tight deadlines for a large dataset and dealing with continuously evolving clinical standards.
Dedicated and trained teams:
Highest process efficiency is assured with:
The patented platform offers benefits:
Trusted healthcare data—sourced ethically, de-identified securely, and delivered with expert quality at scale.
From sourcing and licensing to de-identification and labeling—one partner across the healthcare AI data lifecycle.
Expert support across clinical text, EHRs, medical audio, imaging, and multimodal datasets.
Healthcare-trained specialists—not generic crowd workers.
Consent-driven collection with clear data lineage and auditability.
Strong security practices that protect sensitive healthcare data throughout the workflow.
Multi-layer QA and human-in-the-loop validation for consistent, accurate datasets.
Trusted to deliver large, complex healthcare datasets for enterprise AI programs.
HIPAA Safe Harbor, Expert Determination, and GDPR-aligned de-identification by design.
Named Entity Recognition (NER), identifies and classifies entities such as people, organizations, and locations within unstructured text.
License HIPAA-ready medical datasets across 31 specialties — physician dictation, EHRs, transcribed medical records. Sample on request.
Generative AI and large language models (LLMs) require massive volumes of high-quality training data to produce accurate, reliable, and context-aware outputs.
Empowering teams to build world-leading AI products.
Contact us now to learn how we can collect and annotate dataset for your unique AI/ML solution
Medical data annotation is the process of labeling medical text, images, audio, and video to train AI models. It is crucial for developing accurate AI systems that improve diagnostics, treatment planning, and patient care.
By providing labeled datasets, AI models can learn to recognize patterns in complex medical data, such as identifying diseases in X-rays or extracting key information from clinical notes. This improves the precision and reliability of AI applications in healthcare.
Medical data annotation includes labeling clinical notes, electronic health records (EHRs), X-rays, MRIs, CT scans, pathology reports, and audio data like physician dictations.
Annotated medical text enables natural language processing (NLP) models to extract and interpret clinical information, such as symptoms, diseases, or medications, from unstructured data like physician notes or discharge summaries.
Annotating medical data requires handling unstructured and complex information, ensuring clinical accuracy, and complying with privacy regulations like HIPAA. It also demands expertise in medical terminology and domain knowledge.
Annotation providers follow strict data security protocols such as HIPAA compliance and use de-identified data to maintain patient privacy while annotating sensitive medical information.
Annotated datasets train AI models to recognize disease markers in medical images or text. For instance, AI can identify cancer stages in oncology or detect heart conditions in cardiology, improving early diagnosis and treatment outcomes.
Advanced annotation tools and domain-specific software, such as DICOM viewers for medical imaging, are used alongside human expertise to ensure high accuracy in labeling medical data.
Shaip combines domain experts, advanced annotation tools, and a robust quality assurance process to deliver precise and scalable medical data annotation tailored to client needs. They specialize in radiology, oncology, cardiology, and other healthcare domains.
The cost depends on the type, volume, and complexity of data, as well as the level of expertise required. Shaip provides customized pricing based on specific project requirements.
