Digital
Radiology AI

Medical data labeling is applying structured tags or annotations to datasets such as images, text, or audio to make them usable for AI. iMerit provides multimodal annotation services across imaging, clinical notes, and audio for healthcare AI.

Medical image annotation is the process of labeling or marking areas of interest in clinical imaging datasets, such as CT, MRI, X-ray, ultrasound, and histopathology. These annotations provide the ground truth needed to train AI models for diagnostic and clinical applications.

iMerit supports a wide range of imaging modalities, including X-ray, CT, MRI, ultrasound, mammography, PET, digital breast tomosynthesis, and histopathology, handling both 2D and 3D datasets for clinical-grade AI training.

In radiology, annotation involves highlighting and labeling key anatomical structures or pathologies in imaging studies. For example, annotators may outline tumors in MRI scans, mark calcifications in mammograms, segment organs in CT datasets, or classify findings in an X-ray.

At iMerit, radiologists and domain-trained annotators ensure that annotations meet clinical standards, enabling reliable AI models.

Annotation structures raw medical data into labeled datasets suitable for AI models that assist in diagnostics, triage, and treatment planning. iMerit combines automation, clinical expertise, and compliance-ready workflows to produce accurate, large-scale labeled datasets.

Data labeling tools provide the interface for annotators, while services manage the annotation process. Tools must support clinical workflows, compliance, and specialized formats.

iMerit offers both: Ango Hub as the tool platform and expert-managed annotation services for scalable, high-quality datasets.

Tools vary depending on modality and use case, but must support DICOM, NRRD, NIfTI, and provide features like segmentation, bounding boxes, keypoints, or temporal labeling. iMerit’s Ango Hub does all of the above and provides polygon, brush, bounding box, keypoint, and AI-assisted pre-labeling tools, with multi-resolution zoom and frame-specific annotation.

The best tool depends on workflow requirements. For medical AI, it should handle specialized formats, complex annotations, and comply with HIPAA/GxP standards. Ango Hub combines flexibility, automation, and compliance-ready workflows for healthcare AI annotation projects.

iMerit supports a wide range of imaging modalities, including X-ray, CT, MRI, ultrasound, mammography, digital breast tomosynthesis, PET scans, and histopathology slides. Our platform can handle both 2D and 3D datasets for clinical-grade AI training.

iMerit provides comprehensive annotation capabilities, including bounding boxes, polygons, polylines, keypoints, semantic segmentation, 3D multiplanar annotations, and AI-assisted pre-labeling. Annotations can capture lesions, structures, and multi-object relationships, with support for frame-specific or task-specific labels in videos or multi-frame imaging.

• NRRD: Multidimensional raster data format
• NIfTI: Common in neuroimaging, especially MRI
• DICOM: Standard across all medical imaging modalities

iMerit can deliver annotated datasets in all three formats, plus JSON or custom outputs, depending on client needs.

Challenges include maintaining clinical precision, handling complex modalities, ensuring compliance (HIPAA, FDA, GxP), and scaling across large datasets. iMerit uses dual-shore, tiered teams, expert-in-the-loop reviews, and advanced QA frameworks to balance accuracy, scale, and compliance.

Yes. Annotations can be tailored to specific clinical targets, supporting object-specific, frame-specific, and task-specific labeling. iMerit workflows cover oncology, cardiovascular, pulmonology, gastroenterology, and more, ensuring datasets are ready for AI model development.

All data is annotated and reviewed by certified radiologists, medical experts, and domain-trained annotators. iMerit’s expert-in-the-loop review model, combined with dual-shore and tiered teams, ensures high accuracy across large-scale projects.

iMerit adheres to FDA, HIPAA, GxP standards and maintains SOC 2 and ISO 27001 certifications. All data is processed in secure environments with audit trails, metadata tagging, and rigorous quality control to maintain confidentiality and regulatory compliance.

Yes. iMerit’s Ango Hub platform and workforce are designed for scale, combining automation with clinical expertise to deliver high-quality results efficiently without compromising accuracy.

iMerit uses clinically-informed, project-specific QA frameworks designed for healthcare AI. Our quality systems include consensus workflows, weighted error scoring, and statistical sampling to deliver defensible, measurable results. Reviewer calibration, real-time dashboards, and issue management tools ensure transparency, while expert-led super QC audits provide clinical oversight. 

iMerit employs a combination of certified radiologists, medical experts, and domain-trained annotators to ensure high-quality annotations. This hybrid approach leverages the clinical expertise of radiologists and the efficiency of trained annotators, facilitating accurate and scalable data labeling.

iMerit implements a multi-step quality control process that includes consensus labeling, arbitration models, and tiered pipelines such as label + review + super QC. This structured approach ensures that annotations meet clinical-grade standards and are suitable for AI model training.

Yes. We provide pilot projects with smaller batches and shorter timelines to showcase our quality, workflows, and capabilities. This allows clients to evaluate our services before scaling to larger, long-term projects.

Costs vary by modality, annotation type, complexity, and dataset size. Pricing models include per-task, per-hour, or project-based. iMerit offers custom pricing and pilot projects so clients can evaluate cost and quality before scaling. Contact us to explore the best solution for your project’s needs.

iMerit provides annotated data in a wide range of formats compatible with machine learning workflows, including NRRD, NIfTI, and JSON. Custom formats can also be accommodated upon request with custom plug-ins.