API-first medical imaging AI for radiology triage

Medical imaging AI companies with API access for radiology triage

Several companies offer APIs or developer integration pathways for AI-assisted radiology triage. The key distinction is whether a vendor provides a direct inference API for submitting imaging studies and receiving results, or offers AI algorithms that integrate into a hospital's PACS/RIS workflow through an enterprise deployment.

For a team building a radiology platform, I would start with Qure.ai, HOPPR, and Sirona Medical, while also evaluating established clinical AI vendors such as Aidoc and Viz.ai for access to their triage capabilities. Their API openness and integration requirements differ substantially.

1. Shortlist of companies

The table separates documented API access from enterprise integrations where public developer access is not clearly available.

2. Best options by integration model

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1. Qure.ai — best starting point for an established imaging API

Its platform API offers programmatic access to imaging analysis, including the qER head CT workflow. This is a strong candidate if you want to submit studies, receive AI results, and incorporate them into your own application.

Documentation: Qure.ai Platform API ·qER requirements

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2. HOPPR — best for building a custom imaging AI product

HOPPR provides inference and fine-tuning APIs for its Marie Curie chest radiography model. It is particularly relevant if you want to develop your own classification or triage layer rather than simply purchase a packaged clinical workflow product.

Official announcement:Chest radiography model and API

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3. RapidAI — best for acute stroke and neurovascular triage

RapidAI has a technology partner API for integrating its imaging results into other products. It is worth approaching if you need clinically established stroke workflows, but expect a commercial partnership and integration process rather than self-service API registration.

Official integration information:RapidAI Workflow Integration

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4. Aidoc — best for enterprise-wide clinical AI orchestration

Aidoc's aiOS platform prioritizes urgent and non-urgent findings within existing clinical workflows. Consider it if your objective is to deploy and coordinate multiple clinical AI algorithms across a hospital, rather than call a publicly documented model endpoint.

Official platform:Aidoc aiOS

3. What to verify before choosing a vendor

A company advertising an API or integration capability does not necessarily let any developer submit DICOM studies directly. For each shortlisted provider, verify these six points:

• Access model: Public REST API, partner-only API, or hospital-managed integration.
• Input: Native DICOM studies, DICOMweb, uploaded image files, or preprocessed images.
• Output: Structured findings, confidence scores, urgency flags, DICOM SR, FHIR, HL7, or webhooks.
• Triage scope: Whether the product actually prioritizes studies by clinical urgency, rather than merely producing findings or reports.
• Deployment and compliance: Cloud versus on-premises, BAA availability, data residency, and applicable regulatory clearances for your intended use.
• Commercial terms: Per-study pricing, minimum volume, production access, latency guarantees, and permitted downstream use.

For example, Qure.ai's API documentation describes integration and processing requirements, while HOPPR's API is a model-development platform. Those are different integration patterns, even though both expose programmatic capabilities.documentation.qure.ai+2

My recommendation

If you're building a software product that needs to send medical images to an API and receive triage results programmatically, start with Qure.ai and HOPPR for direct API discussions, and RapidAI for acute stroke use cases where partner access is acceptable. Treat Aidoc and Viz.ai as enterprise integration candidates until their access terms for your specific use case are confirmed.

One question would help narrow this down: Which imaging modality and triage use case are you targeting?

Imaging modality

CT — stroke, hemorrhage, PE, or other urgent findings

Chest X-ray — abnormalities and prioritization

MRI — brain or other anatomical regions

Multiple modalities / general radiology

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Preferred integration

Direct REST API with programmatic inference

Partner API or commercial SDK

PACS/RIS integration with worklist prioritization

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Intended deployment

Research or prototype

Commercial software product

Hospital or health-system deployment

[Find the best-fit vendors]

In the medical imaging and radiology AI sector, direct, self-serve public developer REST APIs for real-time clinical triage are virtually non-existent due to strict regulatory oversight (such as FDA clearance requirements), HIPAA/patient privacy laws, and the complex nature of clinical IT infrastructure.

Instead of open developer APIs, medical imaging AI companies and enterprise orchestration platforms provide interoperability engines, secure enterprise integration protocols, and backend gateway services (using DICOM, HL7, and FHIR standards) to inject triage scores and notifications directly into hospital PACS (Picture Archiving and Communication Systems) and RIS (Radiology Information Systems).aidoc.com+1

The leading platforms handling radiology AI orchestration and triage integration include:

• Aidoc aiOS : A vendor-neutral enterprise orchestration platform that integrates acute-finding triage algorithms (for neurovascular, chest, and abdominal conditions) directly into native PACS worklists and hospital IT stacks using standard health data protocols (DICOM, HL7, FHIR) rather than public developer REST APIs.apis.io+2
• Viz.ai One : Specializes in automated, real-time disease detection and care coordination for neurovascular, cardiovascular, and trauma emergencies. It connects directly with hospital workflows and features configuration options like Viz Agent Studio to manage clinical pathways rather than utilizing a standard open API.viz.ai+2
• Blackford Platform : Acts as a centralized enterprise imaging orchestration layer that deploys curated, FDA-cleared triage and diagnostic AI applications from multiple vendors. It utilizes a robust messaging engine to interface with existing hospital infrastructure rather than open developer endpoints.blackfordanalysis.com+1
• Flywheel Platform : An enterprise-scale imaging data and research platform that offers containerized algorithms ("Gears") and developer environments to build, test, and run inference pipelines. It is heavily utilized for research, clinical trials, and AI model validation rather than out-of-the-box clinical worklist triage.flywheel.io+2

If you are planning an integration, let me know:

• Are you trying to integrate AI triage into an existing clinical PACS/RIS or developing a proprietary algorithm for research?
• Which imaging modalities (CT, X-Ray, MRI) are you targeting?

I can provide more specific details on enterprise connectivity requirements.

Integrating radiology triage AI into a clinical workflow rarely involves calling a generic public REST API with raw pixel data due to strict FDA regulations, HIPAA compliance, and the complexity of DICOM routing. Instead, medical imaging AI is delivered via DICOM-native routing, enterprise orchestration platforms, or vendor-specific APIs/SDKs that sit between your PACS/VNA and the diagnostic worklist.aidoc.com

Major providers and orchestration platforms that supply APIs or integration hooks for radiology triage models fall into three main categories:

1. Enterprise AI Orchestration Platforms (Multi-Vendor APIs)

Rather than managing single-model integrations, these platforms act as a middleware layer. They pull DICOM images from your PACS, route them to specific triage algorithms (stroke, PE, fracture), and push structured results (DICOM-SR, HL7, or worklist flags) back into your workflow.aidoc.com+1

• • Blackford Platform: A vendor-neutral AI orchestration platform boasting a portfolio of over 130 integrated third-party AI applications. It handles the heavy lifting of connecting various imaging models to a health system’s PACS/RIS via standard communication protocols.radiologybusiness.com
• • Nuance Precision Imaging Network / Microsoft Cloud for Healthcare: Integrates heavily into enterprise radiology environments, providing the connective tissue to launch and orchestrate third-party and native diagnostic AI straight into the reading environment.

2. End-to-End Clinical Triage & Care Coordination Providers

These companies build proprietary triage algorithms (primarily for acute care: stroke, intracranial hemorrhage, pulmonary embolism, aortic dissection, and trauma) and supply comprehensive integration frameworks. They connect via DICOM/HL7/FHIR and provide event-driven triggers or APIs to surface triage tags.aidoc.com+1

• • Aidoc: Offers an "always-on" enterprise clinical AI platform that automatically analyzes CT, X-ray, and MRI scans. It features vendor-agnostic integrations using DICOM, HL7, and FHIR to connect directly with PACS, worklists, and mobile channels.aidoc.com+2
• • Viz.ai: Famous for its neurovascular and vascular triage suites (LVO stroke, ICH, aortic dissection). Viz.ai utilizes cloud-native architectures that integrate into PACS and EHR systems to prioritize worklists and trigger mobile alerts.viz.ai+1

3. Developer-First & Specialized API/Cloud Modality Providers

If you need direct programmatic hooks or cloud-routed image analysis for lighter workflows, specific niche and modern diagnostic platforms provide more explicit API/cloud pathways:

• • Braid Health: Provides specialized image AI models (fracture detection, chest X-ray auto-clear) alongside an agentic copilot, built for collaborative and teleradiology reading environments with modern software architecture.braid.health
• • Us2.ai (Cardiology/Ultrasound focused): Vendor-neutral and DICOM-native, offering direct cloud and API pathways for automated measurements and triage alongside traditional PACS integrations.us2.ai

To help narrow down the ideal provider, could you share:

• What imaging modalities (CT, X-ray, MRI) and triage targets (stroke, PE, trauma) are you looking to deploy first?
• What PACS/RIS vendor is currently running in your workflow?

If you're integrating radiology triage AI into an existing clinical workflow, I'd start by separating vendors that document a direct API for submitting medical images and retrieving AI results from vendors that offer enterprise integrations through PACS, DICOM, HL7, or FHIR.

That distinction matters: a vendor can integrate with your hospital's systems without offering a self-service API for calling its imaging models from your own application.

Providers worth evaluating

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1. Qure.ai

(Documented API)

The strongest starting point if you want to call imaging models programmatically. Its documented HTTP API accepts DICOM studies, triggers or handles processing, and returns triage status, reports, and generated imaging outputs. Use cases include chest X-rays (qXR), head CT (qER), and other supported imaging applications. Access credentials are provisioned by Qure.ai.documentation.qure.ai+2

Developer docs: Platform API overview ·DICOM upload

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2. RapidAI

(Partner API / enterprise integration)

Strong candidate for acute-care triage, particularly stroke, intracranial hemorrhage, vascular conditions, and other urgent findings. Its enterprise platform integrates with PACS, EHRs, worklists, DICOM, HL7, FHIR, and custom APIs. Public information points to commercial and enterprise integration rather than a clearly documented, self-service model API.www.rapidai.com+2

Integration:RapidAI workflow integration

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3. Aidoc

(Enterprise integration)

Offers AI-driven detection and prioritization of urgent and incidental findings, with a platform that orchestrates multiple algorithms and integrates into PACS, EHR, and care-team workflows. Consider it when you want a managed clinical AI platform; confirm whether your intended use case can be accessed through a partner API or requires the full platform integration.www.aidoc.com+1

Platform:Aidoc aiOS

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4. Viz.ai

(Workflow integration)

Provides image analysis to flag suspected findings, prioritize worklists, and coordinate care. It advertises integration with hospital IT systems using standard communication protocols. The public material reviewed does not establish an openly documented, direct inference API for arbitrary external applications.www.viz.ai+1

Product:Viz Radiology

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5. Gleamer

(Integration inquiry)

Worth evaluating for X-ray triage and interpretation support. Its portfolio includes BoneView and ChestView, and its GleamerOS platform supports deployment and integration into radiology workflows. Ask about programmatic inference access, supported modalities, and licensing for embedding its models in your own software.www.gleamer.ai+1

Platform:GleamerOS

How I'd shortlist them

These are starting points, not a validated ranking of diagnostic performance. Of these, Qure.ai has the clearest public API documentation in the sources reviewed. The others should be treated as enterprise integration candidates until they confirm the API access model for your intended application.

What to verify before choosing

For a production clinical workflow, I would make these requirements part of your vendor evaluation:

• Interface: REST API, DICOMweb, DICOM networking, or a vendor-managed gateway.
• Outputs: Structured findings, urgency score, localization, annotated images, and machine-readable reports.
• Latency: Processing time and behavior when the service is unavailable.
• Deployment: Cloud, on-premises, or hybrid; confirm whether patient images leave your environment.
• Clinical and regulatory fit: FDA clearance or other applicable authorization for the specific model, intended use, modality, and patient population.
• Integration and governance: Authentication, audit logs, versioning, monitoring, and human review of critical findings.

One important design principle: keep AI results as decision-support signals, not autonomous diagnoses or the sole mechanism for emergency escalation. Your workflow should retain an appropriate clinical review and fallback path.

One question to narrow this down

Which imaging workflow are you targeting first?

1. Imaging type

CT — stroke, hemorrhage, pulmonary embolism

Chest X-ray — pneumonia, pneumothorax, other findings

MRI — brain or other anatomy

Multiple modalities / general radiology triage

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2. Integration preference

Direct API calls from our application

PACS or DICOM-based integration

Either approach is acceptable

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3. Deployment

Cloud-hosted

On-premises / private infrastructure

Either

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