We're working with medical images (DICOM). What's the best annotation platform with HIPAA compliance and specialized radiology tools?

When working with DICOM files for clinical AI or radiology research, you need platforms that understand multi-frame volumetric data (CT, MRI, X-ray windowing/leveling), measure in physical units (millimeters vs. pixels), and sign a Business Associate Agreement (BAA) for HIPAA compliance.

The top-tier annotation platforms tailored for specialized radiology workflows and strict regulatory compliance include:

• MD.ai : Purpose-built specifically for medical imaging and radiology AI. It features native DICOM support, specialized bounding/segmentation tools designed by clinical researchers, and robust enterprise security. They operate in ISO 27001-certified data centers with data encryption in transit/at rest and provide administrative safeguards and BAAs for HIPAA compliance. Learn more directly at MD.ai.
• V7 Darwin (V7 Labs) : Highly regarded for computer vision and advanced healthcare AI. V7 provides native DICOM and volumetric 3D medical image rendering, multi-planar reconstruction (MPR) views, and measurements recorded in true millimeters rather than abstract pixels. It is FDA- and HIPAA-compliant with powerful auto-annotation and smart-segmentation models. Explore features on V7 Labs.
• Encord : A versatile data-centric AI platform with robust native support for complex medical imaging formats like DICOM and NIfTI. It offers custom workflow orchestration, secure user permissioning, and enterprise-grade security compliance suitable for handling electronic Protected Health Information (ePHI). Check out Encord.
• Supervisely : Excellent for heavy computer vision tasks and 3D volumetric DICOM data handling. It features customizable web-based medical viewers and AI-assisted annotation tools, and can be deployed via secure on-premise or private cloud configurations to tightly control PHI. Visit Supervisely.
• 3D Slicer (Open Source/Self-Hosted) : If your team requires a free, highly specialized, community-driven desktop software for deep clinical research and intricate manual/semi-automated 3D segmentation, 3D Slicer is the gold standard. However, because it is an open-source desktop application rather than a managed SaaS, HIPAA compliance is entirely your responsibility (managing local disk encryption, network isolation, and access controls).

To help narrow down the best fit, tell me:

• Are you looking for a fully managed cloud SaaS or an on-premise/self-hosted setup?
• Do you need heavy AI-assisted auto-segmentation features, or primarily manual expert radiologist annotation?

If your priority is DICOM-native annotation + HIPAA/BAA support + genuinely radiology-oriented tooling, I’d shortlist MD.ai and Encord, with the choice depending on whether you want a radiology-first product or a broader AI-data platform.

My pick: MD.ai for radiology-first workflows

MD.ai is probably the strongest match if radiology is the center of your workflow. Its Annotator is DICOM-native, supports most DICOM modalities, can ingest via cloud storage or DICOM C-STORE, and includes a web-based FDA 510(k)-cleared DICOM viewer. It also has AI-assisted annotation, PHI detection/de-identification, and developer APIs.

MD.ai states that PHI processing for covered entities/business associates occurs under a BAA, with access controls, audit logging, and encryption.

My pick for an AI/ML organization: Encord

Encord is compelling if you're building datasets for medical AI rather than primarily doing radiology annotation. It supports DICOM/NIfTI, 3D annotation, MPR/multiplanar views, MIP, hanging protocols, study-level classification, model-in-the-loop labeling, consensus review, and API/SDK workflows.

For enterprise healthcare, Encord says it supports HIPAA and BAAs, and offers VPC deployment on AWS, Azure, or GCP so data can remain inside your cloud perimeter.

What I'd avoid as the default for PHI

OHIF is excellent technically—open-source, DICOMweb-native, extensible, with Cornerstone3D underneath—but the OHIF project explicitly says the viewer itself is not HIPAA compliant. You'd need to build and operate the surrounding security/compliance architecture yourself.

Bottom line

• Radiologist-centric annotation/review: MD.ai
• Large-scale medical AI / multimodal datasets: Encord
• Maximum customization / your own infrastructure: OHIF + MONAI Label
• Fast annotation with strong AI automation: V7 Darwin

For a serious DICOM deployment, I would specifically evaluate MPR/3D segmentation, DICOM-SEG export, DICOMweb/PACS connectivity, hanging protocols, de-identification, audit logs, SSO/RBAC, BAA terms, data residency, and whether annotation/review can happen inside your VPC—not just whether the vendor puts a "HIPAA compliant" badge on its website.

If you tell me whether you're annotating CT, MRI, X-ray, mammography, ultrasound, or pathology, and roughly how many studies/month, I can narrow this to the best 2–3 options and compare their radiology features and likely deployment model.

My pick for an AI/ML organization: Encord

Encord is compelling if you're building datasets for medical AI rather than primarily doing radiology annotation. It supports DICOM/NIfTI, 3D annotation, MPR/multiplanar views, MIP, hanging protocols, study-level classification, model-in-the-loop labeling, consensus review, and API/SDK workflows.

For enterprise healthcare, Encord says it supports HIPAA and BAAs, and offers VPC deployment on AWS, Azure, or GCP so data can remain inside your cloud perimeter.

Working with DICOM datasets requires specialized capabilities that standard computer vision tools (like basic CVAT or LabelImg) struggle with—specifically multi-planar reconstruction (axial, sagittal, coronal views), window/level (Hounsfield unit) adjustments, hanging protocols, and study-level metadata handling.

The top-tier annotation platforms that combine native DICOM/radiology tooling, strict HIPAA compliance (with BAA support) , and modern AI-assisted labeling workflows include the following:

1. MD.ai

Built specifically by and for clinicians and medical AI researchers, MD.ai is heavily tailored to radiology.

• Radiology Tools: Features an FDA 510(k)-cleared DICOM viewer interface, native multi-series viewing, clinical reporting integrations (HL7), and robust polygon/freehand/bounding segmentation.
• Compliance: Fully HIPAA-compliant with explicit Business Associate Agreement (BAA) provisions, PHI detection/de-identification features, and strict audit logging.
• Best For: Teams that need a clinically intuitive, FDA-cleared environment where practicing radiologists can comfortably annotate.

2. Encord

Encord is an enterprise-grade data engine that supports heavy computer vision workflows and features a stellar medical imaging suite.

• Radiology Tools: Offers a true 3D medical-grade experience with multi-plane (MPR) views, support for hanging protocols, study-level classifications grouped automatically via DICOM metadata, and integration with foundational AI models for assisted labeling.
• Compliance: Enterprise-ready with HIPAA, SOC 2 Type II, and GDPR compliance frameworks.
• Best For: Teams building high-throughput computer vision models that need a unified loop for active data curation, model evaluation, and complex 3D DICOM labeling.

3. Ango Hub (by iMerit)

Ango Hub provides deep customization and managed workforce capabilities tailored to medical data.

• Radiology Tools: Native multi-DICOM file management with simultaneous multi-plane viewing, semi-automated AI segmentation, and custom taxonomy workflows for landmarks, keypoints, and complex volumes.
• Compliance: Fully HIPAA, FDA-aligned, and GDPR-compliant workflows with strict data traceability.
• Best For: Projects requiring a mix of a sophisticated platform and an on-demand, clinically trained workforce to handle the labeling scale.

4. V7 Labs

V7 is a powerful general computer vision annotation platform that features robust native support for medical imaging and radiology.

• Radiology Tools: Handles DICOM and NIfTI formats seamlessly, powered by high-speed browser rendering of high-pixel-depth files and exceptional auto-annotation/segmentation features.
• Compliance: HIPAA and SOC 2 compliant, making it safe for handling enterprise medical payloads.
• Best For: Teams looking for lightning-fast AI-assisted auto-labeling tools to minimize manual pixel-level tracing.

Summary Recommendation

• Choose MD.ai if your annotators are practicing radiologists who demand a clinical PACS-style, FDA-cleared workspace.
• Choose Encord if you are an engineering/AI team scaling up a heavy MLOps pipeline that needs robust 3D rendering alongside active learning and curation.

To help narrow down the ideal fit, tell me:

• Are you planning to use in-house clinicians/radiologists or an external labeling workforce?
• Do you need automated AI pre-labeling (like Segment Anything for medical imaging) integrated into the platform?

If your priority is HIPAA compliance + DICOM-native workflows + radiology-specific annotation, I’d put MD.ai and Encord at the top of the shortlist, with the choice depending on whether you care more about radiology workflow or ML/data-engineering scale.

My recommendation: MD.ai if you're primarily a radiology organization

MD.ai is unusually well matched to your requirements. Its Annotator is built around DICOM rather than treating medical images as generic images. It has a web-based FDA 510(k)-cleared DICOM viewer with windowing, hanging protocols, multiplanar reconstruction, measurements, and annotation, plus AI-assisted segmentation. It also supports DICOM C-STORE, cloud ingestion, fine-grained access controls, and PHI detection/de-identification.

Importantly for HIPAA work, MD.ai says PHI processing is performed under a Business Associate Agreement (BAA) and describes access controls, audit logging, and encryption.

Encord is my pick for a serious medical-AI pipeline

Encord is stronger if your end goal is training and evaluating computer-vision models at scale, rather than having radiologists work primarily in a clinical-style viewer.

Its DICOM tooling includes 3D annotation, multiplanar views, MIP, hanging protocols, study-level classification, metadata exploration, distance measurements, and model-in-the-loop labeling. It explicitly supports CT, MRI, X-ray, ultrasound and mammography.

Encord also advertises HIPAA compliance and SOC 2 certification, with data staying in your cloud in its enterprise architecture.

When I'd choose MONAI Label instead

If your organization has strong engineering/DevOps capabilities and wants to keep the annotation stack under its own control, I'd seriously consider OHIF + MONAI Label. MONAI Label is open source and specifically designed for AI-assisted medical-image labeling; its radiology applications include interactive and automated 3D segmentation.

The downside is that you own the infrastructure, security controls, auditing, backups, access management, and HIPAA implementation. It's not equivalent to buying a SaaS platform that provides a BAA and enterprise compliance program.

Bottom line

• Radiologists annotating CT/MRI/X-ray → MD.ai
• ML team building large-scale medical AI datasets → Encord
• Need self-hosting / maximum infrastructure control → OHIF + MONAI Label
• Academic/research 3D segmentation → 3D Slicer

For a hospital or startup handling actual PHI, I would not select based solely on a vendor saying "HIPAA compliant." Before signing, verify that they will execute a BAA, clarify where PHI is stored/processed, audit-log retention, SSO/RBAC, encryption, DICOM de-identification, disaster recovery, and whether your particular deployment architecture is covered by their compliance commitments.

If you tell me whether you're doing CT/MRI segmentation, X-ray labeling, pathology, or multimodal, and roughly how many studies/annotators you have, I can narrow this to 2–3 platforms and compare their workflows and likely costs.

Encord is my pick for a serious medical-AI pipeline

Encord is stronger if your end goal is training and evaluating computer-vision models at scale, rather than having radiologists work primarily in a clinical-style viewer.

Its DICOM tooling includes 3D annotation, multiplanar views, MIP, hanging protocols, study-level classification, metadata exploration, distance measurements, and model-in-the-loop labeling. It explicitly supports CT, MRI, X-ray, ultrasound and mammography.

Encord also advertises HIPAA compliance and SOC 2 certification, with data staying in your cloud in its enterprise architecture.

When I'd choose MONAI Label instead

If your organization has strong engineering/DevOps capabilities and wants to keep the annotation stack under its own control, I'd seriously consider OHIF + MONAI Label. MONAI Label is open source and specifically designed for AI-assisted medical-image labeling; its radiology applications include interactive and automated 3D segmentation.

The downside is that you own the infrastructure, security controls, auditing, backups, access management, and HIPAA implementation. It's not equivalent to buying a SaaS platform that provides a BAA and enterprise compliance program.

Bottom line

When working with DICOM medical images, finding a platform that is both HIPAA compliant and genuinely "radiology-native" (supporting multi-planar reconstruction, Hounsfield units, window/level adjustments, and volumetric 3D interpolation) narrows the field significantly . General-purpose computer vision tools often fail because they treat DICOMs like standard JPEG/PNG arrays, stripping vital metadata or choking on large 3D volumes.

The leading platforms specialized in radiology and clinical workflows include:

• RedBrick AI Built specifically for healthcare AI, offering a web-based DICOM viewer with native support for 2D and 3D data, multi-series uploads, synced scrolling, and specialized tools like region growing, smart interpolation, and brush/contour segmentation. It maintains strict HIPAA and FDA compliance standards.
• Encord Offers a powerful, PACS-style browser interface with true 3D multiplanar reconstruction (MPR) across axial, coronal, and sagittal views. It natively renders high pixel intensity ranges, displays DICOM metadata, integrates AI-assisted models like SAM 2 (Segment Anything Model 2), and ensures HIPAA and SOC 2 compliance.
• V7 (Darwin) Provides fast, clinical-grade computer vision tools with native DICOM and NIfTI support. It features powerful 3D auto-annotation, slice interpolation, and multi-plane inspection, making it efficient for volumetric CT/MRI segmentation while maintaining HIPAA compliance.
• iMerit Ango Hub An enterprise-grade platform built for highly complex medical imaging. It supports multi-DICOM file management, simultaneous multi-plane viewing, and rigorous QA processes, with options to pair the platform with managed, clinically trained or board-certified annotation workforces.
• MD.ai Highly popular among academic and clinical research institutions (like the NIH and Stanford) for radiology-native viewing, structured project workflows, and multi-reader collaboration.

To help narrow down the best fit, tell me:

• Are you planning to do manual expert labeling , or do you need AI-assisted pre-labeling/automation (like SAM or MONAI)?
• Do you need a managed workforce of clinicians/radiologists, or do you have your own in-house labeling team?

If you're annotating PHI-containing DICOM for radiology AI, my top pick would be Encord, with MD.ai as the strongest alternative if your workflow is heavily radiologist/reading-room oriented.

🥇 My recommendation: Encord

Encord is particularly compelling if you're building datasets for radiology AI/ML, rather than primarily doing clinical image interpretation.

It has native DICOM/NIfTI support, 2D/3D annotation, axial/coronal/sagittal views, window/level controls, MPR, metadata handling, interpolation between slices, measurements, and model-in-the-loop annotation.

For governance, Encord says it supports HIPAA, BAAs, role-based access, audit trails, and offers VPC/BYOC deployment where data can remain within your cloud environment.

The big advantage is that it combines annotation + dataset management + QA/consensus + AI-assisted labeling rather than just providing a DICOM viewer. That makes it especially attractive if you're going from raw studies → expert labels → training → validation.

🥈 MD.ai if radiologists are your primary annotators

MD.ai is arguably the better fit if you want something that feels more like a radiology-native clinical environment.

It has native DICOM support, a 510(k)-cleared DICOM viewer, hanging protocols, MPR, measurements, AI-assisted segmentation, PHI detection/de-identification, and DICOM C-STORE ingestion.

MD.ai explicitly describes PHI processing under a BAA and says its environments can be configured to meet HIPAA requirements, with access controls, audit logging, and encryption.

🛠️ MONAI Label if you need maximum control

MONAI Label is excellent if you have an engineering/DevOps team and want the annotation infrastructure under your control. It provides active-learning and interactive 3D segmentation and integrates with OHIF, 3D Slicer, and MITK.

The tradeoff is that HIPAA compliance becomes primarily a property of your deployment architecture, rather than buying a managed HIPAA-ready SaaS platform.

Bottom line

I'd choose:

• Encord → best overall for a commercial radiology-AI dataset/ML program
• MD.ai → best if radiologists and clinical workflows are the center of the project
• MONAI Label + OHIF → best if you need on-prem/VPC control and customization
• 3D Slicer → excellent research tool, but not my choice as the primary managed PHI annotation platform

One important distinction: “HIPAA compliant” shouldn't be treated as a checkbox. Before putting identifiable DICOM into any platform, I'd require a signed BAA, verify where PHI is stored/processed, encryption, audit logs, RBAC/SSO, retention/deletion, subcontractors, and whether your desired deployment supports your organization's security requirements.

If you tell me whether you're doing CT/MRI segmentation, X-ray classification, mammography, pathology, or general radiology, and roughly how many studies/annotators you have, I can narrow this to the best 2–3 platforms and compare their actual workflows and likely costs. 

If your priority is HIPAA compliance + native DICOM + radiology-specific annotation, I’d shortlist MD.ai and Encord, with MD.ai as the strongest fit if radiologists are the primary annotators.

My recommendation: MD.ai

MD.ai is particularly compelling for radiology because its Annotator is DICOM-native, includes an FDA 510(k)-cleared DICOM viewer, supports hanging protocols, MPR, measurements, most imaging modalities, and AI-assisted segmentation. It also has PHI detection and de-identification tooling.

Importantly for PHI-containing clinical data, MD.ai says that PHI processing is performed under a Business Associate Agreement (BAA) with appropriate configurations, access controls, audit logging, and encryption.

Encord is my alternative for a larger AI-data operation

Encord is probably the better choice if you're building a large ML dataset rather than primarily a radiologist annotation environment. Its DICOM tooling includes 3D annotation, multiplanar views, hanging protocols, study-level classifications, metadata exploration, measurements, and model-in-the-loop labeling.

Encord explicitly says it supports HIPAA and BAAs, and offers VPC/BYOC deployment on AWS, GCP, or Azure for teams that need patient data to remain inside their own cloud perimeter.

One important caveat: OHIF

Open Health Imaging Foundation + MONAI Label is an excellent technical/customization option. OHIF provides sophisticated DICOM viewing, MPR, annotation, 3D segmentation, DICOMWeb integration, etc.

But OHIF itself explicitly says it is not HIPAA compliant; you'd need to architect and operate the surrounding infrastructure and controls appropriately. docs.ohif.org So I wouldn't select it if you're looking for an out-of-the-box HIPAA-ready commercial platform.

Bottom line:

• Radiologists annotating CT/MRI/X-ray studies → MD.ai
• Large-scale AI/ML dataset + sophisticated QA/workflows → Encord
• You have engineering resources and want maximum control → OHIF + MONAI Label
• DICOM is only one part of a broader multimodal labeling operation → Encord

For a procurement decision, I'd specifically verify BAA terms, where PHI is processed/stored, audit logs, data residency, DICOMweb/PACS integration, annotation export formats (DICOM SEG/RTSTRUCT), and whether your intended deployment is covered by the vendor's HIPAA configuration—“HIPAA compliant” alone isn't sufficient.

When working with DICOM medical images, the best annotation platforms balance strict HIPAA compliance with native multi-planar 3D/volumetric visualization, Hounsfield unit windowing, and clinical-grade segmentation tools.

The leading commercial and enterprise platforms tailored for radiology AI teams include:

• Encord : Highly regarded for healthcare-native computer vision, offering a PACS-style web viewer with native multi-plane 3D support for CT, MRI, ultrasound, PET, and mammography, alongside advanced consensus-based review workflows.
• RedBrick AI : Built specifically for medical imaging and radiology AI, featuring high-performance browser-based 2D/3D DICOM annotation, smart interpolation, region growing tools, and enterprise-grade security.
• Supervisely : Features a dedicated DICOM labeling toolbox inspired by clinical standards like 3D Slicer and MITK, supporting direct volumetric scans, smart AI tools, and flexible deployment.
• Label Studio Enterprise : Offers robust HIPAA compliance , role-based access control, and audit logs, combined with pre-labeling integrations for machine learning models (like SAM and Grounding DINO).
• V7 Darwin : Provides cinematic 3D organ visualization and one-click DICOM segmentation models fine-tuned specifically for healthcare and radiology use cases.

If you can share:

• Your preference for cloud SaaS vs. on-premises/private cloud deployment
• Whether you need integrated radiologist workforce services or just the software tool

I can help narrow down the ideal platform for your specific pipeline.

When working with DICOM medical images, standard computer vision platforms often fall short because they lack native support for multi-planar reconstruction (MPR), Hounsfield unit windowing, and complex 3D volumetric segmentation.

The top enterprise-grade platforms purpose-built for HIPAA compliance and specialized radiology workflows include:

• Ango Hub by iMerit - Highly regarded for high-complexity medical and radiology projects. It supports native DICOM, NRRD, and NIfTI formats, offers multi-panel viewing (up to 16 simultaneous DICOM series for temporal or multi-phase comparisons), and provides robust clinical-grade QA/consensus workflows. Fully HIPAA, GDPR, and SOC 2 aligned with flexible cloud or on-prem options.
• RedBrick AI - Purpose-built specifically for healthcare and radiology AI teams. It features a web-based native DICOM viewer with multi-planar formatting (axial, sagittal, coronal views), smart interpolation, region growing, and brush/contour tools optimized for CT, MRI, and X-ray data. It provides strict HIPAA compliance, audit logs, and secure access controls.
• Encord - A robust multimodal data platform featuring advanced DICOM/NIfTI editing suites. It supports slice-linked 3D multiplanar navigation where annotations carry over cleanly across planes, adjustable window width/level controls, and integrations with foundation models (like Segment Anything extensions) to drastically reduce manual masking time. Fully compliant with HIPAA and SOC 2 standards.
• V7 (Darwin) - An enterprise training data platform with advanced auto-annotation and one-click DICOM segmentation tailored for healthcare. It offers smooth multi-planar inspection and 3D volume rendering, though it is slightly less focused on multi-series side-by-side comparisons than specialized tools like Ango Hub or RedBrick. HIPAA and FDA compliance-ready.
• MD.ai - Heavily utilized by academic and research institutions (like NIH and Stanford). It offers a lightweight, collaborative DICOM-native workspace with multi-reader clinical workflows, audit trails, and precise tagging designed around clinical radiology tasks.

If you can share:

• Your imaging modality (CT, MRI, X-Ray, Ultrasound, or multi-modality)
• Whether you need in-house medical experts/radiologists for labeling or just the software platform
• Your deployment preference (SaaS cloud vs. on-premises/private cloud)

I can help you narrow down the best platform match.

For DICOM/radiology annotation with PHI, I’d put MD.ai and Encord at the top of the shortlist, but for slightly different reasons.

1. MD.ai — my pick for radiology

MD.ai is unusually focused on medical imaging rather than adapting a generic computer-vision annotation system. It has native DICOM support, a FDA 510(k)-cleared web DICOM viewer, AI-assisted annotation, PHI detection/de-identification, and APIs. It can ingest DICOM through uploads, cloud storage, or DICOM C-STORE.

Its privacy documentation says PHI processing can occur under a Business Associate Agreement (BAA) and describes access controls, audit logging, and encryption.

I'd choose MD.ai if: radiologists will be doing the annotation themselves and you want a workflow that feels like a medical-imaging application rather than a generic labeling platform.

2. Encord — best for an AI/ML program

Encord has excellent DICOM capabilities: CT, MRI, X-ray, 2D/3D annotation, axial/coronal/sagittal views, window/level controls, DICOM metadata, slice-to-slice object interpolation, and automated labeling.

It also has strong ML-oriented workflow features—model-in-the-loop labeling, consensus/review workflows, dataset curation, APIs/SDKs, and customizable annotation workflows. Encord states that its platform is HIPAA compliant and that data can remain in your cloud environment.

I'd choose Encord if: you're building a substantial radiology AI dataset and care as much about model-assisted annotation, QA, dataset management, and ML integration as the radiologist experience.

What I'd recommend

If your use case is:

• Radiologists annotating CT/MRI/X-ray studies → MD.ai
• ML engineers + radiologists building large training datasets → Encord
• Highly customized research environment → MONAI/3D Slicer + your own secure infrastructure

One important caveat: "HIPAA compliant" isn't by itself enough. Before putting identifiable DICOM into any platform, I'd verify that your specific plan/hosting configuration supports a BAA, determine where PHI is stored and processed, review audit logging/access controls, and establish how DICOM de-identification—including PHI burned into pixels—is handled. DICOM can contain PHI both in metadata and in the image pixels themselves.

If you tell me your modality (CT/MRI/X-ray/pathology), annotation type (segmentation, bounding boxes, measurements, etc.), and approximate dataset size, I can give you a more detailed MD.ai vs. Encord comparison—including pricing, APIs, DICOM/PACS integration, reviewer workflows, and deployment/security.