Reduce diagnostic turnaround time by 60% and improve anomaly detection accuracy with AI models integrated directly into your PACS and radiology workflows.
Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Deploy advanced computer vision models to automate detection, segmentation, and analysis in X-rays, MRIs, and CT scans.
Reduce diagnostic turnaround time by 60% and improve anomaly detection accuracy with AI models integrated directly into your PACS and radiology workflows.
Our engineers specialize in building high-accuracy pipelines for 3D medical volumes, whole-slide pathology images, and multi-modal fusion. We ensure models are trained on domain-specific, de-identified datasets and deployed with 99.9% inference uptime SLAs to support critical clinical decisions.
Move beyond pilot projects to production-scale AI. Explore our broader capabilities in Healthcare Clinical Decision Support and Ambient AI or learn how we ensure compliance with Healthcare AI Compliance and Governance Consulting.
We integrate state-of-the-art computer vision models into your existing radiology and pathology workflows, delivering measurable improvements in diagnostic speed, accuracy, and operational efficiency.
Seamless integration of leading open-source frameworks like MONAI and nnU-Net for automated detection, segmentation, and quantitative analysis of anomalies in X-rays, MRIs, and CT scans. We ensure models are optimized for your specific hardware and data formats.
Direct, bi-directional integration with your Picture Archiving and Communication System (PACS) and Electronic Health Record (EHR). AI findings are embedded as structured data, enabling seamless clinician review without disrupting established workflows.
End-to-end secure data pipelines designed for Protected Health Information (PHI). All data is encrypted in transit and at rest, with strict access controls and comprehensive audit logs to ensure compliance with HIPAA and SOC 2 Type II standards.
Rigorous validation of model performance against real-world, annotated datasets specific to your institution. We provide detailed performance reports on sensitivity, specificity, and AUC to support clinical trust and regulatory readiness.
Development of intuitive clinician interfaces that present AI-generated findings as actionable overlays and quantitative measurements. Supports easy correction, feedback loops for model improvement, and confidence scoring.
Deployment of containerized inference services on Kubernetes, ensuring 99.9% uptime and automatic scaling to handle peak imaging volumes. Includes GPU optimization and hybrid cloud architecture for cost-efficiency.
A structured roadmap for integrating deep learning models into your radiology or pathology workflow, from initial data assessment to full clinical deployment.
| Phase & Key Deliverables | Timeline | Core Activities | Outcome |
|---|---|---|---|
Discovery & Data Assessment | 1-2 Weeks | Clinical workflow analysis, data availability & quality audit, regulatory scope definition | Project charter & technical specification document |
Model Selection & Prototyping | 2-4 Weeks | Evaluation of pre-trained models (e.g., MONAI, nnU-Net) vs. custom training, proof-of-concept development on sample data | Validated model architecture & performance baseline report |
Data Pipeline & Annotation Engineering | 3-6 Weeks | HIPAA-compliant data ingestion pipeline, development of annotation protocols with clinical experts, synthetic data augmentation | Production-ready, de-identified training dataset & annotation toolkit |
Model Training & Validation | 4-8 Weeks | Distributed training on GPU clusters, rigorous validation against hold-out & external datasets, bias & fairness auditing | FDA-ready model validation report with performance metrics (e.g., sensitivity, specificity) |
Clinical Integration & API Development | 3-5 Weeks | Development of DICOM-compliant REST/gRPC APIs, integration with PACS/VNA, user interface (UI) prototyping for radiologists | Staging environment with integrated AI inference endpoint & clinician UI |
Pilot Deployment & Clinical Feedback | 4-6 Weeks | Limited live pilot with selected clinicians, structured feedback collection, iterative model & UI refinements | Clinical usability report & refined deployment package |
Full Deployment & Support Handoff | 1-2 Weeks | Production deployment, monitoring dashboard setup, documentation, and knowledge transfer to your IT/clinical engineering team | Fully operational system with 99.9% uptime SLA & ongoing support plan |
We deploy advanced computer vision models into your existing clinical workflows using a structured, risk-mitigated process designed for healthcare environments. Our methodology ensures seamless integration, regulatory compliance, and measurable improvements in diagnostic speed and accuracy.
We conduct a deep-dive analysis of your current radiology and pathology workflows to identify integration points, data silos, and potential bottlenecks. This ensures the AI solution augments, rather than disrupts, clinician productivity.
We select and rigorously validate state-of-the-art frameworks like MONAI or nnU-Net against your de-identified historical data. Performance is benchmarked for accuracy, specificity, and sensitivity to meet clinical safety standards before deployment.
We build encrypted, auditable data pipelines that ingest DICOM images from PACS/VNA systems. All data processing adheres to HIPAA and HITRUST principles, with PHI de-identification and strict access controls. Learn about our approach to Confidential Computing for AI Workloads.
Our engineers deploy inference APIs and embed AI-powered visualization tools directly into your existing PACS (e.g., Epic Radiant, Sectra) or custom viewer interfaces. This provides radiologists with AI insights without switching applications.
We implement live monitoring dashboards to track model drift, inference accuracy, and system uptime. Our MLOps pipelines enable periodic retraining on new, approved data to maintain peak performance, similar to our AIOps practices.
We provide tailored training for radiologists, technologists, and IT staff to ensure effective adoption. Our support includes interpreting AI outputs, troubleshooting, and integrating findings into clinical reporting workflows for maximum impact.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Common questions about integrating deep learning for medical imaging, from timelines and security to our development methodology.
Standard integration projects for a single imaging modality (e.g., chest X-ray) take 4-6 weeks from data pipeline setup to clinical workflow integration. Complex multi-modality projects (CT, MRI, pathology) typically require 8-12 weeks. Our phased approach includes 2 weeks for data assessment and model validation, ensuring a predictable deployment schedule. For rapid prototyping, we offer a 2-week proof-of-concept using your sample data.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
