RadFlow AI — AI-Powered Radiology Workflow Assistant | Codebridge

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RadFlow AI — AI-Powered Radiology Workflow Assistant | Codebridge

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# RadFlow AI content (Cisco 7-layer rewrite) Codebridge's RadFlow AI platform cut average CT reading time by 38% for a Tier-1 diagnostic imaging network operating 12 radiology centers across three US states, reducing readings from 15.2 to 9.4 minutes per study while maintaining 96% nodule detection sensitivity for sub-4mm lesions and running in production for over 9 months without reported critical system failures. The engagement spanned 24 weeks with an 8-person Codebridge team, following a discovery phase in which scan volumes had been growing 22% year-over-year against flat radiologist headcount, pushing turnaround times 15% beyond contractual SLAs and degrading detection accuracy during late-shift reads. "Human-in-the-loop" was the design mandate. As one clinical leader summarized during discovery: "We didn't need another black-box algorithm. We needed a workspace that supports how radiologists actually think and work." The client's COO added: "This initiative was not about automation — it was about capacity expansion without compromising diagnostic integrity. The platform allowed us to increase throughput while strengthening compliance posture and long-term regulatory readiness." The pain point was a layered system problem rather than a single bottleneck: radiologists operated across disconnected systems (primary PACS viewer, separate AI interface, voice-dictation reporting), consuming roughly one-third of total reading time on non-interpretive tasks; high-resolution chest CT studies generated hundreds of DICOM instances exceeding several hundred megabytes, causing load-time latency at rural teleradiology sites on satellite connections; and prior commercial AI pilots produced elevated false-positive volumes that eroded clinician trust — Radiologist Trust Score had been 27%. During a three-week clinical discovery phase, Codebridge ran time-motion analysis and regulatory gap assessment, then delivered the platform in phases: platform foundation with DICOMweb integration and SAML SSO (weeks 4-8), AI model training on large-scale CT datasets (weeks 9-14), multi-site clinical pilot with shadow-mode validation (weeks 15-18), and independent clinical validation plus staged 12-site rollout (weeks 19-24). The solution passed an independent clinical validation study of n=2,400 scans with a double-blind design. The platform is a browser-based diagnostic workspace synchronized with the client's Vendor Neutral Archive, built on OHIF and Cornerstone.js with GPU-accelerated WebGL 2.0 rendering, progressive DICOM streaming (DICOMweb WADO-RS/QIDO-RS/STOW-RS), and adaptive bandwidth compression for satellite sites. AI inference runs asynchronously on study ingestion using a 3D Feature Pyramid Network with ResNet-50 backbone, with average end-to-end latency of ~47 seconds per CT study. Infrastructure: FastAPI backend, PostgreSQL for clinical metadata, Redis for caching, RabbitMQ for async orchestration, and NVIDIA Triton Inference Server on AWS EKS with GPU auto-scaling for seasonal peaks. The development lifecycle is aligned with IEC 62304, ISO 13485, and FDA Software as a Medical Device (SaMD) Class II regulatory pathways, with immutable audit logging of every AI-assisted decision. Operational results include: false positive rate reduced from 4.1 to 0.8 per scan initially and further to 0.4 after 9 months of active learning; Radiologist Trust Score rising from 27% to 89% within six months; and sub-second image rendering maintained even on low-bandwidth satellite connections. The client processes over 500 chest CT scans weekly with seasonal spikes exceeding 700 during peak respiratory periods. Codebridge continues to develop similar AI agent and computer-vision solutions across healthtech and logistics, with a stated future focus on explainability controls such as Grad-CAM saliency maps and confidence threshold configuration.