
Enterprise Imaging AI Platform for an Outpatient Radiology Network
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Overview
What we built
An outpatient radiology network had bought seven AI tools that barely spoke to its radiologists. We built one governed platform that puts every result where it belongs: the worklist.
In plain terms: the network's 15 centres used AI tools from different vendors to help read scans, but the plumbing behind them was chaos. Each of the seven tools came with its own contract, its own interface and its own integration, and findings from most of them never reached the list radiologists actually work from. Nobody checked whether the algorithms still performed well after go-live, and adding a new one took around four months of bespoke work each time.
We replaced the tangle with a single vendor-neutral platform. Imaging studies now flow from every scanner to the right third-party algorithm automatically, and results land directly in the PACS worklist the radiologists already use. A central console handles onboarding, access control, uptime and drift monitoring, and audit logging. Onboarding a new algorithm now takes 3 weeks instead of roughly 4 months, results from all 7 tools reach the worklist, up from 2, and consolidation cut overlapping licence and integration spend by 23%.
The Problem
Seven disconnected AI tools
The network had grown to 15 outpatient centres running scanners from different brands, and AI had arrived one purchase at a time. Radiology leadership would trial a promising point tool, sign a contract, and hand IT another bespoke integration project. By the time we were engaged, seven such tools were live, each with its own interface, its own support arrangement and its own way of moving images.
The clinical value was leaking away at the last step. Findings from most of the tools never reached the radiologist worklist: results sat in separate viewers and portals that nobody had time to open mid-shift. A radiologist reading a study had no reliable way of knowing which algorithms had already looked at it, or what they had found.
Governance was thinner still. Once a tool went live, nobody monitored how its models performed, so a quietly degrading algorithm would simply keep producing output with no one watching. And every new algorithm the network wanted to adopt meant around four months of bespoke integration work before a single study flowed through it.
Seven separate silos
Each AI tool ran on its own contract, interface and integration, multiplying vendor management effort across every one of the network's centres.
Results off the worklist
Findings from most tools never reached the radiologist worklist, leaving output stranded in side viewers that readers rarely had time to open.
No post-go-live monitoring
Nobody tracked model performance once a tool went live, so drift and outages could run unnoticed until a clinician happened to spot something odd.
Slow algorithm onboarding
Bringing a new algorithm into service took around four months of bespoke integration work, throttling the network's ability to adopt anything new.
What it was costing them
Every scan read without AI findings on the worklist diluted the value of seven paid contracts, and overlapping licence and integration spend kept climbing with each new tool. Radiologists lost time hopping between viewers, IT carried a growing pile of fragile bespoke integrations, and unmonitored models meant a clinical risk the network could neither see nor measure.
The Solution
One governed AI pipeline
We designed the platform around one principle: vendor-neutral routing. Imaging studies from every scanner, regardless of brand, flow into a single pipeline that identifies each study and dispatches it to the appropriate third-party algorithms. When a result comes back, the platform writes it directly into the PACS worklist, so the radiologist sees AI findings in the same place they read the study.
Around the pipeline we built a central console that turns governance from an afterthought into the default. Onboarding a new algorithm is a configuration exercise rather than a bespoke project, access control decides who can see and manage what, uptime and drift monitoring watch every model continuously, and audit logging records what each algorithm received and returned.
The result is one governed pipeline in place of seven separate silos. The network keeps the freedom to adopt algorithms from any vendor, but every tool now enters service, runs and eventually retires through the same managed path, with the same monitoring and the same audit trail sitting behind all of it.
Key decisions
Vendor-neutral by design
The platform routes studies to any third-party algorithm rather than favouring one vendor, so clinical teams choose tools on merit while the integration cost stays flat.
Results go to the worklist
Every AI finding is written into the PACS worklist radiologists already use, instead of another standalone viewer competing for their attention.
One console for governance
Onboarding, access control, monitoring and audit logging live in a single console, so running seven tools feels like running one system.
Monitor models after go-live
Uptime and drift monitoring run continuously on every algorithm, treating post-deployment performance as an operational duty rather than a launch-day checkbox.
Onboarding as configuration
New algorithms join through a standard onboarding path in the console, replacing the bespoke integration projects that previously took around four months each.
Measurable Impact
What changed after launch
The platform changed the economics of adopting imaging AI. Onboarding a new algorithm dropped from roughly 4 months of bespoke work to 3 weeks, and consolidating contracts and integrations cut overlapping licence and integration spend by 23%. Results from all 7 tools now arrive in the radiologist worklist, up from 2 before the engagement.
Governance moved from wishful to measurable. Drift and uptime monitoring flagged 3 model performance regressions in the first year, each caught by the platform rather than by a clinician noticing something wrong. With audit logging on every study routed and every result returned, the network can finally answer who used which algorithm, when, and with what outcome.
Worklist delivery
AI findings reached the worklist from only 2 tools
Results from all 7 tools delivered into the worklist
Algorithm onboarding
Around 4 months of bespoke integration work
A governed onboarding path taking 3 weeks
Licence spend
Seven overlapping contracts and integrations
Consolidated pipeline, overlapping spend down 23%
Model oversight
No monitoring after go-live
Continuous checks flagged 3 regressions in the first year
Headline results
New algorithm onboarding time reduced from roughly 4 months to 3 weeks
AI results now delivered into the radiologist worklist for all 7 tools, up from 2
Overlapping licence and integration spend cut by 23% after consolidation
Drift and uptime monitoring flagged 3 model performance regressions in the first year
Tech & Tools Used
What powered the build
Every tool below earned its place in this engagement. Here is the part each one played.
Python (FastAPI)
Powers the routing services at the heart of the platform, deciding which algorithms each incoming study should visit and posting their findings back towards the worklist.
Orthanc DICOM Server
Receives imaging studies from scanners across the network's centres and stages them for dispatch, giving the platform one consistent DICOM front door regardless of scanner brand.
DICOMweb & HL7 FHIR
The interoperability standards the platform speaks: DICOMweb moves imaging to and from third-party algorithms, while FHIR carries structured findings into downstream clinical systems.
RabbitMQ
Queues studies between ingestion and algorithm dispatch, smoothing out scanning peaks so bursts of studies never overwhelm slower third-party services.
Kubernetes
Runs the platform's routing, integration and console services as containers, scaling them with imaging volume and restarting anything unhealthy without manual intervention.
PostgreSQL
Stores the platform's routing rules, algorithm registry and audit log, providing the durable record behind every study routed and every result returned.
Prometheus + Grafana
The monitoring pair behind the console: Prometheus collects uptime and drift signals from every algorithm, and Grafana turns them into the dashboards that surfaced the regressions.
Keycloak
Provides role-based access control across the console, defining who at each centre can onboard algorithms, view results or review audit trails.
React
The framework behind the central console interface where teams onboard algorithms, manage access and watch the monitoring dashboards.
Azure
Hosts the platform's services and storage in the cloud, providing the managed infrastructure underneath the routing pipeline and central console.
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