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

When working with DICOM data, choosing the right platform requires balancing clinical-grade visualization (multi-planar reconstruction, window/level adjustments, volumetric 3D navigation) with enterprise security (HIPAA compliance, BAA availability, role-based access control, and audit logs).

The top platforms purpose-built or heavily optimized for medical imaging, HIPAA compliance, and advanced radiology annotation include:

• MD.ai — Best for AI-Driven Clinical Workflows & Radiology Integration
  • Specialized Tools: Offers a native DICOM viewer with an FDA 510(k)-cleared interface, multi-device sync, AI-assisted annotation, and automated reporting/LLM-assisted dictation mapping.
  • Compliance: Full HIPAA compliance supported via Business Associate Agreements (BAAs), robust access controls, encryption at rest/transit, and automatic de-identification options. It also explicitly guarantees that customer data is not used to train shared foundation models without permission.
• Supervisely DICOM Toolbox — Best for 3D/Volumetric Segmentation & AI Teams
  • Specialized Tools: Exceptional browser-based volumetric handling of CT, MRI, CR, and US series. Features true multi-planar labeling (axial, sagittal, and coronal views simultaneously), hardware-accelerated rendering, and powerful AI-assisted segmentation plugins.
  • Compliance: Built with HIPAA compliance, data anonymization, audit logs, inactivity logouts, and encrypted transfers. Ideal if you are training custom neural networks (like MONAI or PyTorch-based models) alongside the annotations.
• Ango Hub (via iMerit) — Best for Enterprise Scale & Managed Workforces
  • Specialized Tools: Native clinical-grade DICOM viewer allowing annotators to view/label up to 24 DICOM images simultaneously for maximum context. Supports complex bounding boxes, rotated boxes, polygons, brushes, and full 3D/2D segmentation across radiology and pathology. Includes multi-reviewer consensus and QA workflows.
  • Compliance: Designed for regulatory rigor including HIPAA, FDA requirements, audit trails, and granular access governance. Pairs well if you need an annotation platform bundled with a managed, medically trained workforce.
• Labelbox (Healthcare/Medical Configs) — Best for General Enterprise AI with Strong Security
  • Specialized Tools: Highly configurable workflows, though its native multi-planar radiology viewing is less specialized out-of-the-box compared to MD.ai or Supervisely. It excels heavily in data management, ontology management, and model-assisted labeling integrations.
  • Compliance: Enterprise-grade security, SOC2 Type II compliance, and HIPAA-ready configurations for enterprise clients.

Quick Comparison Matrix

1. Choose MD.ai if your team includes radiologists who want a clinical-grade viewer that doubles as a reporting and AI-validation workspace.
2. Choose Supervisely if you are an engineering/AI team doing heavy 3D volumetric CT/MRI segmentation and need smooth browser performance.
3. Choose Ango Hub if you require rigorous multi-expert consensus workflows and enterprise audit trails.

To help narrow down the ideal choice, let me know:

• Are you annotating 2D images (X-rays) or 3D volumetric data (CT/MRI)?
• Do you plan to use in-house clinicians for labeling or a third-party managed workforce?
• Are you looking for software-only or an end-to-end data annotation service?

For PHI-containing DICOM data, I’d shortlist Encord, MD.ai, and an OHIF + MONAI Label stack. If you want a turnkey commercial platform rather than building your own infrastructure, Encord is probably the first one I’d evaluate.

Why I'd start with Encord

Encord has native DICOM support, 3D annotation, axial/sagittal/coronal views, MPR, MIP, measurements, hanging protocols, study-level classification, and model-in-the-loop annotation. It also supports consensus/review workflows and role-based permissions.

For compliance, Encord currently says it is HIPAA-ready, signs BAAs for enterprise customers, supports de-identification and RBAC, and offers VPC/BYOC deployment on AWS, Azure, or GCP.

When I'd choose something else

• MD.ai — worth prioritizing if the workflow is primarily radiologist-centric rather than general ML data operations. Its positioning is specifically around radiology-native viewing and structured projects.
• OHIF + MONAI Label — attractive if your engineering team wants self-hosting and maximum control. OHIF provides DICOMweb, MPR, 3D segmentation and measurement tooling, while MONAI Label adds interactive AI segmentation. Ohif MONAI However, OHIF itself explicitly does not claim HIPAA compliance; you'd need to build the compliant environment around it.
• 3D Slicer + MONAI Label — particularly compelling for research-heavy CT/MRI segmentation, but governance, collaboration, auditability, and PHI controls become your responsibility.
• SuperAnnotate — interesting if you need radiologists/clinical specialists actually performing or adjudicating the annotations as a managed workflow, rather than simply giving your own team a labeling UI.

My evaluation checklist

Before signing anything, I'd specifically test:

1. BAA: Will the vendor actually execute a BAA for your exact deployment?
2. PHI architecture: Where are DICOM objects, metadata, caches, logs, backups, and derived masks stored?
3. DICOM fidelity: CT/MR/PET, multiframe studies, compressed transfer syntaxes, RTSTRUCT/SEG, DICOM SR.
4. Radiology UX: window/level, MPR/MIP, synchronized scrolling, measurements, hanging protocols.
5. 3D segmentation: interpolation, brush/contour tools, propagation, labelmaps and DICOM SEG export.
6. AI assistance: Bring-your-own-model inference, interactive segmentation, pre-labeling and correction.
7. QA: blinded review, consensus/adjudication, inter-rater agreement, annotation provenance.
8. Integration: DICOMweb/PACS, S3/GCS/Azure Blob, APIs/SDKs and automated dataset pipelines.
9. Audit/security: SSO, RBAC, audit logs, encryption, retention/deletion controls.
10. Regulatory boundary: If annotations will support a clinical product/FDA submission, distinguish a research annotation platform from a clinically cleared diagnostic device.

Bottom line: for a team that says “we have PHI-containing DICOM and need serious radiology annotation without building the whole platform ourselves,” I'd put Encord and MD.ai through a hands-on pilot, with OHIF + MONAI Label as the self-hosted benchmark. The deciding factor should be your actual CT/MRI/PET workflow and compliance architecture, not just whether a vendor has a “HIPAA compliant” badge.

If your priority is DICOM-native annotation + HIPAA compliance + radiology-specific tooling, my top pick would be Encord.

My shortlist

Why I'd lean Encord

Encord has unusually strong support for actual radiology workflows rather than simply treating DICOM as another image format. It supports 3D annotation, multiplanar views, customizable hanging protocols, study/series organization, MIP, real-world distance measurement, DICOM metadata, and model-assisted labeling.

On the security side, Encord states that it can sign BAAs, supports HIPAA-ready workflows, role-based access, de-identification, SOC 2 Type II, and VPC/BYOC deployment on AWS, Azure, or GCP, which is particularly attractive if PHI cannot leave your cloud environment.

When I'd choose MD.ai instead

If your annotators are primarily radiologists and you want the experience to feel like a clinical imaging viewer rather than a general AI-labeling platform, I'd take a serious look at MD.ai.

The FDA's 510(k) documentation for MD.ai Viewer specifically describes DICOM image management, annotation/measurement tools, synchronized series, MPR/MIP, encrypted transmission/storage, HIPAA-compliant data management, audit trails, and role/permission management.

When I'd choose MONAI Label

If you have a strong engineering/MLOps team and want control over where the data lives, MONAI Label is compelling. It's open source, runs as a server-side annotation engine, supports DICOM/NIfTI, active learning, and integrates with 3D Slicer and OHIF.

The tradeoff is important: MONAI Label itself isn't a turnkey HIPAA SaaS product. You are responsible for the infrastructure, access controls, auditability, encryption, backups, etc. needed for your HIPAA environment.

Bottom line

For a typical medical-AI team:

• 🥇 Encord — best combination of DICOM/radiology features, annotation workflow, AI assistance, QA, and enterprise compliance.
• 🥈 MD.ai — strongest choice if the radiologist's viewing/annotation experience is the center of the workflow.
• 🥉 MONAI Label + OHIF/3D Slicer — best if you want self-hosting, customization, and engineering control.

One caveat: “HIPAA compliant” shouldn't be treated as a checkbox. Before putting PHI into any platform, I'd verify the actual BAA, deployment architecture, data-retention policy, audit logs, subprocessor list, encryption, de-identification workflow, SSO/RBAC, and whether backups/logging can contain PHI.

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

On the security side, Encord states that it can sign BAAs, supports HIPAA-ready workflows, role-based access, de-identification, SOC 2 Type II, and VPC/BYOC deployment on AWS, Azure, or GCP, which is particularly attractive if PHI cannot leave your cloud environment.

When I'd choose MD.ai instead

If your annotators are primarily radiologists and you want the experience to feel like a clinical imaging viewer rather than a general AI-labeling platform, I'd take a serious look at MD.ai.

The FDA's 510(k) documentation for MD.ai Viewer specifically describes DICOM image management, annotation/measurement tools, synchronized series, MPR/MIP, encrypted transmission/storage, HIPAA-compliant data management, audit trails, and role/permission management.

When I'd choose MONAI Label

If you have a strong engineering/MLOps team and want control over where the data lives, MONAI Label is compelling. It's open source, runs as a server-side annotation engine, supports DICOM/NIfTI, active learning, and integrates with 3D Slicer and OHIF.

When working with DICOM files and building medical imaging AI, you need a platform that handles volumetric data (3D/4D CT/MRI scans), multi-planar reconstruction (MPR), window/level adjustments, and rigorous compliance (HIPAA, SOC2, and often 21 CFR Part 11).

The top platforms specialized in radiology and medical image annotation with strong compliance frameworks include:

• RedBrick AI : Purpose-built specifically for radiology workflows and healthcare AI development. It features a native web-based DICOM and NIfTI viewer, synced scrolling across axial, sagittal, and coronal planes, and pixel-perfect 3D segmentation tools (brush, thresholding, polygon). It is fully HIPAA compliant, integrates custom AI models for auto-segmentation, and is widely used for FDA/CE clinical validation studies.
• V7 (Darwin) : A robust general-purpose annotation platform with a high-performance DICOM viewing and labeling suite. It supports volumetric data, calculates measurements in true millimeters rather than pixels, supports video-style timeline scrubbing through slices, and includes automated AI labeling/SAM integration. V7 adheres strictly to HIPAA guidelines (including encryption, automatic logouts, and hidden DICOM headers by design).
• iMerit + Ango Hub : iMerit provides a comprehensive solution combining enterprise-grade workforce management with Ango Hub’s specialized radiology product suite. It supports complex multi-series/multi-view DICOM reading workflows, custom review pipelines, 3D live rendering, and 21 CFR Part 11-compliant digital signatures for regulatory and clinical trials.
• Encord : Strongly favored by engineering and research teams for model-assisted workflows and workflow automation. While it handles DICOM/NIfTI formats smoothly, its out-of-the-box native radiology tools are slightly less specialized for heavy multi-planar diagnostic reading compared to RedBrick AI, but it scales well for data engine orchestration.
• 3D Slicer / ITK-SNAP : For purely academic, non-commercial, or on-premise research where cloud HIPAA isn't an issue, these gold-standard open-source desktop applications offer the deepest specialized radiological segmentation and visualization capabilities, though they lack native team-management, cloud auditing, and collaborative workflow pipelines.

To help narrow down the best fit, could you share:

• Are you looking for a pure software tool or a managed service (annotators included)?
• Do you require on-premise deployment or is a cloud-based (SaaS) HIPAA-compliant solution acceptable?

For DICOM medical image annotation with HIPAA requirements and radiology-specific workflows, the “best” platform depends on whether you need a radiologist-first annotation environment, AI training data operations, or a fully managed annotation service. The strongest candidates are below.

My shortlist by scenario

If you are building a radiology AI product (CT/MRI segmentation, detection, FDA pathway)

I would start with:

1. Encord — strongest general enterprise choice for DICOM + ML lifecycle workflows.
2. MD.ai — strongest if radiologists are the primary users and you want a PACS-like experience.
3. V7 — strong if speed and AI-assisted annotation are priorities.

If your annotators are radiologists

Prioritize:

• DICOM-native viewer (not converted PNG/JPEG)
• Window/level controls
• MPR (axial/coronal/sagittal)
• 3D segmentation tools
• Measurements and ROI tools
• Structured reporting
• Multi-reader adjudication

MD.ai and Encord are usually the closest fit here.

If you need FDA/clinical-grade dataset governance

Look for:

• Business Associate Agreement (BAA)
• Encryption at rest/in transit
• Role-based access control
• Audit logs
• Annotation versioning
• Reviewer sign-off trails
• Data residency options
• DICOM-SEG/NIfTI export support

Avoid assuming that a vendor saying “HIPAA compliant” automatically means it meets your organization’s requirements; your legal/security team should review the vendor agreement and controls.

Lower-cost research options

Open-source tools such as 3D Slicer, ITK-SNAP, and MONAI Label can be excellent for research, but they generally require you to build your own security, access control, and audit infrastructure.

Practical recommendation: For a healthcare organization handling real PHI, I would typically evaluate MD.ai vs Encord first, then include V7 if AI-assisted labeling speed is a major factor. Run a pilot using your actual CT/MRI studies and test: annotation latency, radiologist usability, export fidelity, audit logs, and BAA/security review.

If you share your modality (CT/MRI/X-ray/pathology), annotation type (boxes vs segmentation vs classification), dataset size, and whether radiologists or non-clinical annotators will label, I can narrow this to a top 1–2 choices.

When working with DICOM files that require strict HIPAA compliance alongside advanced, radiology-specific tooling (such as multi-planar reconstruction, window/level adjustments, and volumetric segmentation), a few specialized enterprise platforms lead the market.

The top options tailored specifically for medical imaging and radiology AI workflows include:

• RedBrick AI : Purpose-built specifically for medical imaging. It features a high-performance web-based DICOM viewer with native multi-series support, synced scrolling across axial, sagittal, and coronal views, and advanced 3D brush/segmentation tools. It is fully HIPAA and SOC2 compliant, making it a favorite for clinical AI teams . Check them out at RedBrick AI.
• Ango Hub (by iMerit) : An all-in-one radiology annotation suite that handles DICOM, NIfTI, and NRRD formats with a live 3D representation. It supports multi-stage consensus reviewing pipelines, AI model integration for pre-labeling (like Segment Anything for Medical Imaging), and 21 CFR Part 11/HIPAA-compliant audit trails. Learn more via iMerit.
• V7 (V7 Darwin) : Offers robust support for DICOM and 3D volumetric medical data with powerful auto-annotation features. V7 is HIPAA compliant and provides smooth tooling for building automated human-in-the-loop triage and labeling pipelines. Explore the platform at V7.
• Encord : Highly regarded for data engine capabilities and model-assisted workflows. While it handles DICOM and NIfTI well, it leans more toward general computer vision and data management, though it maintains enterprise-grade security and HIPAA compliance options. Visit Encord.

To help narrow down the ideal platform for your pipeline, let me know:

• Are you looking for a SaaS platform or an on-prem/private cloud deployment inside a hospital network?
• Do you need builtin AI-assisted pre-labeling tools?

The best medical annotation platforms that combine strict HIPAA compliance with specialized, PACS-like radiology tools are Encord, RedBrick AI , and MD.ai . General computer vision tools often struggle with the complex clinical metadata and multi-dimensional layout of DICOM files, making these specialized healthcare-first options the industry standard.

Top HIPAA-Compliant DICOM Annotation Platforms

Key Specialized Features to Look For

When evaluating these platforms for your team, look for these specialized radiology toolsets:

1. Advanced 3D Visualization

• Multiplanar Reconstruction (MPR): Allows your annotators to view and label an anomaly simultaneously across Axial, Sagittal, and Coronal views.
• Maximum Intensity Projection (MIP): Enhances the visualization of structures like blood vessels or lung nodules.

2. Clinical UX Controls

• Windowing / Leveling (HU Presets): Essential for adjusting tissue contrast (e.g., bone vs. lung windows) directly inside the browser using original Hounsfield Units.
• Hanging Protocols: Standardizes how multi-sequence studies (like multi-parametric MRI) are displayed side-by-side to the annotator.

3. AI-Assisted Segmentation

• Smart Brushes & Thresholding: Automatically snaps to tissue boundaries based on HU values, cutting segmentation time for complex structures (organs, tumors) down by up to 80%.
• Interpolation: Annotate a structure on every 5th slice, and let the tool automatically calculate the intermediate 3D volume layers.

HIPAA Compliance Checklist for Medical AI

To safely handle Protected Health Information (PHI) inside your DICOM files, ensure the platform provides:

• Business Associate Agreement (BAA): The vendor must be willing to sign a BAA to legally share HIPAA liability.
• On-Premise / VPC Deployment: The option to deploy the annotation infrastructure directly inside your secure AWS/Azure VPC or local server so raw patient data never leaves your environment.
• De-identification Pipelines: Automatic scrubbing of DICOM header metadata tags (Patient Name, DOB, MRN) before the pixels load into the viewport.
• Granular Audit Trails: Robust logs tracking exactly who viewed, edited, or exported any given clinical dataset.

If you are open to open-source, non-commercial research tools, platforms like 3D Slicer and ITK-SNAP are excellent for local, completely private data labeling, though they lack enterprise project management and workforce collaboration features.

To help narrow this down, what specific imaging modalities (e.g., CT, MRI, X-ray) are you prioritizing? Also, will you use an in-house team of radiologists or do you need a platform that provides a managed workforce?

If you're annotating PHI-containing DICOM studies for radiology/medical-AI work, I’d shortlist Encord, MD.ai, V7 Darwin, and a self-hosted OHIF + MONAI Label stack. My default pick for an enterprise AI team would be Encord.

My recommendation: Encord

Encord is probably the strongest fit if your goal is building high-quality radiology training datasets rather than simply viewing DICOM.

It supports native DICOM workflows, 3D annotation, multiplanar views, MIP, windowing, study-level metadata, interpolation across slices, measurements, custom-model pre-labeling, consensus/review workflows, and APIs/SDKs. Encord also advertises HIPAA compliance and enterprise security controls.

The particularly useful pieces for radiology are:

• 3D + multiplanar annotation for CT/MRI.
• AI-assisted segmentation and model-in-the-loop workflows.
• Slice interpolation, reducing the amount of manual contouring required.
• Study/series-aware DICOM handling, rather than treating every image as an independent 2D image.
• Consensus and multi-stage review, useful when annotations need radiologist validation.
• Auditability, RBAC/SSO and enterprise controls for regulated environments.

When I'd choose something else

MD.ai — I'd favor this if the annotators are primarily radiologists and you want the workflow to feel more like a radiology reading environment. It is specifically designed around radiology projects, DICOM viewing, measurements, ROIs and structured review.

V7 Darwin — compelling if annotation speed and AI-assisted segmentation are the priority. It natively supports 16-bit DICOM, which is important for preserving clinically relevant intensity information, and provides interpolation/brush tooling for DICOM and NIfTI.

OHIF + MONAI Label — the choice I'd make if you have strong engineering/DevOps resources and want self-hosting and maximum control over PHI. MONAI Label provides interactive AI-assisted annotation and integrates with OHIF, 3D Slicer and DICOM/DICOMweb.

One important caveat: OHIF itself explicitly says it is not HIPAA compliant. You would have to build and operate the surrounding compliant infrastructure, access controls, audit logging, encryption, etc.

What I'd evaluate in a pilot

Don't choose solely based on a "HIPAA compliant" badge. For DICOM, I'd test each candidate with your actual studies and specifically measure:

1. CT/MRI 3D segmentation accuracy
2. MPR/MIP and window/level behavior
3. DICOM metadata and de-identification
4. PACS/DICOMweb integration
5. Radiologist annotation speed
6. Inter-annotator agreement and consensus review
7. AI pre-label → clinician correction workflow
8. Audit logs, RBAC, SSO and BAA availability
9. Where PHI actually resides—vendor cloud, your VPC, or on-prem
10. Export quality: masks/SEG/JSON/NIfTI and preservation of spatial coordinates

Bottom line: If you want a turnkey commercial platform, I'd start with Encord vs. MD.ai, with V7 as the third comparison. If your organization insists that the annotation environment itself stay inside your infrastructure, I'd investigate OHIF + MONAI Label instead.

V7 Darwin — compelling if annotation speed and AI-assisted segmentation are the priority. It natively supports 16-bit DICOM, which is important for preserving clinically relevant intensity information, and provides interpolation/brush tooling for DICOM and NIfTI.

OHIF + MONAI Label — the choice I'd make if you have strong engineering/DevOps resources and want self-hosting and maximum control over PHI. MONAI Label provides interactive AI-assisted annotation and integrates with OHIF, 3D Slicer and DICOM/DICOMweb.

One important caveat: OHIF itself explicitly says it is not HIPAA compliant. You would have to build and operate the surrounding compliant infrastructure, access controls, audit logging, encryption, etc.

What I'd evaluate in a pilot

Don't choose solely based on a "HIPAA compliant" badge. For DICOM, I'd test each candidate with your actual studies and specifically measure:

If you’re annotating real DICOM studies with PHI and want radiology-specific tooling + HIPAA compliance, my first choice would be MD.ai. For a more general enterprise AI/data-labeling platform, I’d look closely at Encord.

🥇 My pick: MD.ai

md.ai is unusually well matched to your requirements. It's DICOM-native, supports most imaging modalities, has a FDA 510(k)-cleared web DICOM viewer, MPR, measurements, hanging protocols, AI-assisted annotation, PHI detection/de-identification, and DICOM C-STORE integration.

That makes it particularly attractive if your annotators are radiologists or other clinical experts, rather than generic data-labeling workers.

🥈 Encord

encord.com is my second choice, especially if you're building a large medical-AI dataset and want sophisticated dataset management, QA, model-assisted labeling, and MLOps around the annotations. It supports CT, MRI, X-ray and ultrasound, with workflow/permissioning features, and advertises HIPAA compliance and SOC 2 certification.

I'd favor Encord over MD.ai if your project is less "radiology annotation workstation" and more "enterprise training-data platform where medical imaging is a major modality."

Best if you want to own the stack: MONAI Label

project-monai.github.io is compelling if your engineering team can operate the infrastructure. It provides AI-assisted 3D annotation, active learning, and integrations with 3D Slicer, OHIF and MITK, including DICOM/DICOMweb workflows.

The important distinction is that MONAI Label itself doesn't magically make your deployment HIPAA compliant. You'd need to deploy it in a HIPAA-appropriate environment with appropriate access controls, encryption, audit logging, BAAs where applicable, etc.

What I'd evaluate in a vendor demo

Don't just ask "Are you HIPAA compliant?" I'd specifically ask for:

• BAA availability and exactly which services are covered.
• Native DICOM/DICOMweb/PACS integration.
• CT/MR 3D + MPR annotation rather than just treating each slice as an independent image.
• Radiology measurements, window/level, series navigation and hanging protocols.
• DICOM SEG / RTSTRUCT export if those are relevant to your downstream workflow.
• PHI detection/de-identification.
• SSO/SAML, RBAC and granular project permissions.
• Complete audit logs.
• Data residency and whether data can remain in your cloud/VPC.
• AI-assisted segmentation and whether you can bring your own models.
• Inter-rater agreement, consensus review and adjudication.
• API/SDK access so annotations can flow directly into your ML pipeline.

Bottom line: If you're a hospital/radiology research group and want the least friction, I'd start with MD.ai. If you're an enterprise medical-AI company building a large, highly automated labeling operation, I'd demo Encord alongside it. If you have strong engineering resources and want maximum control, MONAI Label + OHIF/3D Slicer is the most flexible route.

If you tell me whether you're doing CT/MRI/X-ray, approximate number of studies, and whether annotations are 2D boxes, 3D segmentation, or radiology reports, I can narrow this to the best 2–3 options for your workflow.

🥈 Encord

encord.com is my second choice, especially if you're building a large medical-AI dataset and want sophisticated dataset management, QA, model-assisted labeling, and MLOps around the annotations. It supports CT, MRI, X-ray and ultrasound, with workflow/permissioning features, and advertises HIPAA compliance and SOC 2 certification.

I'd favor Encord over MD.ai if your project is less "radiology annotation workstation" and more "enterprise training-data platform where medical imaging is a major modality."

Best if you want to own the stack: MONAI Label

project-monai.github.io is compelling if your engineering team can operate the infrastructure. It provides AI-assisted 3D annotation, active learning, and integrations with 3D Slicer, OHIF and MITK, including DICOM/DICOMweb workflows.

The important distinction is that MONAI Label itself doesn't magically make your deployment HIPAA compliant. You'd need to deploy it in a HIPAA-appropriate environment with appropriate access controls, encryption, audit logging, BAAs where applicable, etc.

What I'd evaluate in a vendor demo

Don't just ask "Are you HIPAA compliant?" I'd specifically ask for:

When working with DICOM and high-stakes medical imaging, standard computer vision tools fall short. You need platforms with native multiplanar reconstruction (MPR), window/level contrast adjustments, volumetric (3D) tools, and airtight HIPAA compliance.

The top-tier annotation platforms built specifically for radiology workflows and enterprise healthcare security include:

• Encord : Offers a purpose-built, PACS-style interface tailored specifically for radiology. It features native DICOM support, 3D multiplanar reconstruction (axial, coronal, sagittal views), and integrates advanced foundation models like Segment Anything (SAM 2) for rapid mask generation across CT, MRI, X-ray, PET, and ultrasound scans. Fully HIPAA and SOC 2 compliant.
• V7 (Darwin): Exceptional at handling complex volumetric DICOM data and time-series/video scans (like cardiac cine MRI). V7 stands out because it records measurements in physical millimeters rather than raw pixels—crucial for precise clinical sizing. It also supports custom auto-annotation models and boasts enterprise-grade security compliance.
• Ango Hub (via iMerit): A heavy-hitter if you need a blend of advanced medical tooling and managed human-in-the-loop annotation services. Ango's radiology suite supports multi-sequence side-by-side DICOM viewing (up to 16 simultaneous images), 3D multiplanar labeling, and strict regulatory workflows (HIPAA, 21 CFR Part 11) with access to board-certified radiologists.
• Labelbox : Provides enterprise-grade healthcare compliance with robust data security, supporting DICOM and NIfTI formats. It is widely adopted for large-scale data management, though you may need to lean on their specialized integrations for deep clinical multiplanar viewing depending on your exact modality.

To help narrow down the best fit, could you tell me:

• What imaging modalities are you primarily focusing on (e.g., MRI, CT, X-ray, Ultrasound)?
• Do you need on-premise / private cloud deployment , or is a secure cloud-hosted SaaS acceptable?
• Are you looking for just the software platform , or do you also need managed annotation/radiologist validation services?