DICOM and DIMSE
C-STORE, C-FIND and C-MOVE for PACS and modality traffic, Modality Worklist for scheduled procedures, and transfer syntax handling so compressed studies decode correctly.
Radiology & Imaging
Diagnostic Imaging AI Integration
FDA-cleared models connected to PACS, the worklist and the viewer, with the radiologist making every diagnosis.
We integrate diagnostic imaging AI into radiology workflows: routing studies to FDA-cleared models over DICOM and DICOMweb, returning results to PACS as DICOM SR and SEG, prioritizing the radiologist worklist, tracking follow-ups and validating each model on your own studies.
Workflow scoping · Evaluation design · Human oversight · Integration planning
Updated September 2026
What diagnostic imaging AI integration involves
Diagnostic imaging AI integration connects image analysis models to the systems radiologists already use. Studies route from PACS to the right FDA-cleared model, results return as standard DICOM objects that any viewer can display, flagged studies move up the worklist, and follow-up recommendations are tracked. The radiologist reviews the AI output as decision support and signs every report.
How diagnostic imaging AI fits the radiology workflow
- 1
Study arrives from the modality
CT, MR, X-ray or mammography studies land in PACS or the vendor-neutral archive over DICOM C-STORE. An HL7 v2 ORM or FHIR ServiceRequest carries the order and clinical indication.
- 2
Routing rules select studies
A router matches each study to the right model by modality, body part, series description and protocol, and skips studies the model is not cleared for, such as pediatric patients or unsupported scanners.
- 3
Model inference
The FDA-cleared model runs on the selected series, pulled over DICOMweb WADO-RS or DICOM C-MOVE, in your cloud or on premises next to PACS.
- 4
Results returned as DICOM objects
Findings come back as DICOM Structured Reports (TID 1500 measurement reports), segmentations as DICOM SEG, and overlays as presentation states, stored with the study so any viewer can show them. The IHE AI Results profile defines this exchange.
- 5
Worklist prioritization and notification
Studies flagged for suspected critical findings move up the radiologist worklist and, where the model is cleared for triage and notification, an alert reaches the on-call radiologist.
- 6
Radiologist reads and decides
The radiologist reviews the images with the AI output as decision support and signs the report. The AI never issues a diagnosis or a final report.
- 7
Follow-up tracking
Recommendations for incidental findings, such as a pulmonary nodule follow-up CT, are extracted from signed reports and tracked until the follow-up is done or closed by a clinician.
Imaging standards we integrate: DICOM, DICOMweb, HL7 and FHIR
DICOMweb
QIDO-RS to search studies, WADO-RS to retrieve instances and rendered frames, and STOW-RS to store AI results back to the archive over HTTPS.
HL7 v2 and FHIR
HL7 v2 ORM orders and ORU reports to the RIS and EHR, and FHIR ImagingStudy, DiagnosticReport and ServiceRequest for EHR-side context and results.
IHE profiles and viewers
IHE AI Results (AIR) and AI Workflow for Imaging (AIW-I) for how results are produced and displayed, and web viewers built on OHIF and Cornerstone for zero-footprint review.
The FDA clearance boundary: radiology AI as decision support
Software that analyzes medical images is a medical device. We keep a clear line between the regulated model and the integration we build around it.
Models carry their own clearance
Image analysis models reach the market through FDA 510(k), De Novo or PMA, for example as radiological computer-aided triage and notification software under 21 CFR 892.2080. We integrate cleared models within their labeled indications, or support your team through its own submission.
The integration layer
Routing, worklist prioritization, result storage, viewer overlays and follow-up tracking present the model's output to a radiologist, who makes the diagnosis. We document intended use so the regulatory team can confirm the integration does not extend the cleared indication.
Model updates
Model changes follow the manufacturer's clearance, including any Predetermined Change Control Plan under FDA's final PCCP guidance for AI-enabled device software. Every model version is recorded against every result.
Workflow tools outside device scope
Protocol assistance, report drafting from the radiologist's own dictated findings and follow-up tracking can be built as workflow tools, with the radiologist editing and signing every report.
Local validation and monitoring
Validation on your studies
Before go-live, the model runs silently on a retrospective set from your scanners and population. Radiologist ground truth gives local sensitivity and specificity, reported by scanner vendor, protocol, age group and sex.
Silent mode in production
The model runs on live studies without showing results for a set period, so disagreement with radiologist reads is measured before anyone relies on it.
Drift monitoring
Flag rates, radiologist agreement and input changes such as a new scanner or protocol are tracked per model, with alerts when they shift.
Radiologist feedback
Radiologists can mark each result as agree or disagree from the viewer, and those labels feed the monitoring dashboard.
Delivery plan and timeline
- 1
Discovery (2 to 3 weeks)
PACS, VNA, RIS and viewer inventory, models and clearances in scope, network paths and the validation data set.
- 2
Integration build (4 to 6 weeks)
Router, DICOMweb and DIMSE connections, result storage as SR and SEG, worklist and notification hooks.
- 3
Validation and silent mode (4 to 8 weeks)
Retrospective validation, then silent operation on live studies with radiologist review.
- 4
Go-live and monitoring
Results shown to radiologists for one study type, with drift dashboards and a rollback switch.
What Drives the Cost of Imaging AI Integration
Imaging estate
Number of PACS, archives, RIS and viewers, and how each exposes DICOMweb or DIMSE.
Models and vendors
Each model or AI platform adds routing rules, result formats and validation.
Compute placement
Cloud inference versus GPU servers on premises next to PACS.
Validation scope
Size of the retrospective set, radiologist labeling time and silent-mode duration.
Proof and further reading
Imaging and interface engineering
Our team has built 350+ interfaces on Mirth Connect, the engine many radiology departments use to move HL7 v2 orders and reports between RIS, PACS and the EHR.
Radiology integration with Mirth ConnectMirth Connect services
Security and contracts
We sign BAAs, and Nirmitee is ISO 27001:2022 certified.
Guides from our engineers
DICOM and FHIR imaging interoperability, and how AI agents fit healthcare workflows.
DICOM and FHIR imaging interoperabilityAll healthcare AI agents
Diagnostic Imaging AI Integration FAQs
What is diagnostic imaging AI?
Diagnostic imaging AI is software that analyzes medical images such as CT, MR, X-ray and mammography to detect, measure or prioritize findings for a radiologist. The model output is decision support; the radiologist reviews the images and signs the report.
Is radiology AI regulated by the FDA?
Yes. Software that analyzes medical images is a medical device. Imaging models reach the US market through 510(k), De Novo or PMA, for example as computer-aided triage and notification software under 21 CFR 892.2080. We integrate cleared models within their labeled indications.
How does imaging AI integrate with PACS?
Studies are routed from PACS or the modality over DICOM C-STORE or DICOMweb, the model runs on the selected series, and results return as DICOM Structured Reports, SEG objects and presentation states stored with the study, so the radiologist sees them in the existing viewer.
What is DICOMweb and why does it matter for AI?
DICOMweb is the RESTful part of the DICOM standard: QIDO-RS to search, WADO-RS to retrieve and STOW-RS to store. It lets AI services pull only the series they need and push results back over HTTPS, which is simpler and more secure than opening DIMSE ports across networks.
Does imaging AI replace the radiologist?
No. The radiologist reads every study and signs every report. Imaging AI flags, measures and prioritizes so critical findings are read sooner and measurements are consistent, and the radiologist can agree or disagree with each result from the viewer.
What is the IHE AI Results profile?
IHE AI Results (AIR) defines how AI outputs are encoded as DICOM SR, SEG and related objects, stored in the archive and displayed consistently across viewers. Following it keeps results portable when you change models or viewers.
How do you validate imaging AI before go-live?
We run the model silently on a retrospective set from your own scanners and population, compare it with radiologist ground truth, and report sensitivity and specificity by scanner, protocol, age group and sex. A silent period on live studies follows before radiologists see results.
Can imaging AI run on premises?
Yes. Inference can run on GPU servers next to PACS so images never leave the network, or in your cloud account under a BAA. The router, result storage and monitoring work the same way in both setups.
How are results written to the EHR?
Signed radiology reports flow to the EHR as HL7 v2 ORU messages or FHIR DiagnosticReport, with ImagingStudy links to the images. AI outputs stay with the study in PACS as decision support for the radiologist.
How long does an imaging AI integration take?
About 10 to 17 weeks for the first model: 2 to 3 weeks of discovery, 4 to 6 weeks of integration build, and 4 to 8 weeks of validation and silent mode. Additional models reuse the router and result storage.
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Scope your imaging AI integration
Bring your PACS, archive, viewer and the models you are evaluating. We will map the routing, result formats, validation set and silent-mode plan for your first model.