AI / Machine Learning11 months16 engineers2024FDA Cleared

Medical Imaging AI Platform

How we built a deep learning radiology platform that achieves 97.3% diagnostic accuracy — deployed across 40 hospital networks in the US and EU

Client

MedCore Health

Compliance

HIPAA · GDPR · FDA 510(k)

Deployment

40 Hospital Networks

97.3%

Diagnostic Accuracy

10.7x

Faster Reports

Hospital Networks

94%

Miss Rate Reduction

Overview

MedCore Health is a leading radiology network operating 40 hospitals across the US and EU. Radiologists were overwhelmed — each reading 80–100 scans per shift with a 48-hour report turnaround. Missed findings were costing lives and triggering costly litigation. They needed AI that could assist, not replace, their radiologists.

Duration

11 months

Team Size

16 engineers

The Challenge

The existing workflow relied entirely on manual radiologist review with no AI assistance. Report turnaround averaged 48 hours, critical findings were missed in 6.2% of cases, and radiologist burnout was at an all-time high. The team had tried two off-the-shelf AI tools that failed FDA validation and were abandoned. They needed a custom, explainable AI that radiologists would actually trust and use.

Our Solution

We designed a multi-modal deep learning platform trained on 4.2 million annotated scans across CT, MRI, and X-ray modalities. The system generates explainable heatmaps showing exactly why it flagged a finding, integrates directly into existing PACS workflows via DICOM, and triages critical findings for immediate radiologist review. A federated learning architecture ensures patient data never leaves hospital premises.

Clinical Impact

In the first 6 months post-launch, the platform prevented an estimated 340 critical missed findings — cases that would have gone undetected under the previous manual workflow. The federated architecture ensures zero patient data ever leaves hospital premises.

Before vs. After

The clinical numbers that matter. Here's exactly what changed after 11 months of work.

Before Codingace.ai

Report Turnaround Time

48 hours

Critical Finding Miss Rate

6.2%

AI Diagnostic Accuracy

Scans Processed/Day

1,200

Radiologist Burnout Score

8.4/10

Litigation Costs/Year

$3.2M

After Codingace.ai

Report Turnaround Time

4.5 hours

Critical Finding Miss Rate

0.4%

AI Diagnostic Accuracy

97.3%

Scans Processed/Day

18,000

Radiologist Burnout Score

3.1/10

Litigation Costs/Year

$0.2M

10.7x

Faster Reports

15x

More Scans/Day

94%

Miss Rate Reduction

$3M

Litigation Savings

Platform Screenshots

A look inside the clinical AI platform we built for MedCore Health.

AI Radiology Reading Interface

Project Timeline

11 months, 5 phases, FDA-cleared. Here's how we built a clinical-grade AI platform.

Phase 1Weeks 1–5

Clinical Discovery & Data Audit

Embedded with radiologists across 6 hospitals to map workflows, identify failure modes, and audit 4.2M historical scans for training data quality and annotation gaps.

Workflow analysis reportData quality auditAnnotation gap analysisRegulatory strategy (FDA 510k)

Phase 2Weeks 6–14

Federated Infrastructure

Built the federated learning infrastructure allowing model training across hospital nodes without centralizing patient data — a hard requirement for HIPAA and GDPR compliance.

Federated learning frameworkDICOM integration layerHIPAA-compliant data pipelineHospital node deployment

Phase 3Weeks 15–28

Model Development & Validation

Trained and validated multi-modal deep learning models for CT, MRI, and X-ray. Developed explainability layer with Grad-CAM heatmaps. Achieved 97.3% accuracy on held-out validation set.

Multi-modal AI modelsExplainability heatmapsClinical validation studyFDA 510k submission

Phase 4Weeks 29–36

PACS Integration & Radiologist UX

Integrated AI findings directly into existing PACS viewers via DICOM SR. Built the radiologist-facing overlay UI with confidence scores, heatmaps, and one-click finding confirmation.

PACS plugin (DICOM SR)Radiologist overlay UITriage queue systemWorkflow automation

Phase 5Weeks 37–44

Phased Rollout & Monitoring

Phased rollout starting with 3 pilot hospitals, expanding to all 40 sites. Continuous model monitoring with drift detection and automated retraining pipelines.

Pilot hospital launchFull 40-site rolloutModel monitoring dashboardRetraining pipeline

Tech Stack

Clinical-grade technology choices — every decision driven by safety, accuracy, and compliance.

AI/ML

PyTorchMONAIGrad-CAM

Frontend

ReactTypeScript

Backend

PythonFastAPI

Healthcare

DICOM / HL7FHIR R4

Infrastructure

Federated LearningKubernetes

Cloud

GCP

Database

PostgreSQL

Monitoring

Prometheus

Clinical Testimonial

"We had tried two other AI vendors and both failed. Codingace.ai was different — they embedded with our radiologists, understood the clinical workflow, and built something our team actually trusts. The 97.3% accuracy isn't just a number; it's lives saved. We've already prevented 340 critical missed findings in the first six months."

Dr. Marcus Chen

Chief of Radiology, MedCore Health

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