Executive Summary
Streamline image triage, boost diagnostic accuracy, and enable real-time reporting across high-volume radiology networks.
The AI solution, trained on de-identified multi-center datasets and fine-tuned with radiologist-labelled pathologies, achieved >92% sensitivity in detecting critical findings, enabled automatic prioritization of high-risk scans, and cut radiology reporting turnaround times by 50%.
Organizational Profile
The client is a top 5 hospital chain in Asia with over 100 diagnostic centers, managing upwards of 20,000 scans per week. With limited radiologist bandwidth and increasing caseloads, especially in rural or Tier-2 centers, delays in interpreting high-priority scans had become a significant clinical risk.
To address this, they partnered with GenPhase.ai to build a real-time AI triage and interpretation assistant that integrates into PACS systems and supports faster, more consistent radiologic decision-making
Business Challenge
Key issues faced in radiology operations:
- Critical Findings Missed or Delayed due to overburdened radiologists
- High Turnaround Time for routine and emergency scan reviews
- Inefficiencies in scan routing and prioritization in centralized PACS systems
- Variability in Reporting across radiologists, impacting second opinions and audit trails
This affected patient outcomes, regulatory compliance (TAT SLAs), and operational throughput.
Technical Solution
The team built an end-to-end AI radiology pipeline. Components included:
- Multimodal Vision Foundation Model trained on millions of X-rays, CT, and MRI images from public and client datasets
- Task-specific Fine-tuning for pathologies like pneumothorax, fractures, intracranial bleeds, lung nodules, and organ anomalies
- Auto-Triage Layer to prioritize scans based on criticality (e.g., STAT chest X-ray with pneumothorax)
- Federated Learning Module for secure, privacy-preserving model improvement across hospital sites
- Explainable AI Interface with saliency maps and region highlighting for radiologist trust
- Structured Report Generation from findings using NLP-based summarization
Implementation Approach
Phase 1: Dataset Curation & Annotation
- Curated a diverse dataset of DICOM scans across modalities and vendors
- Radiologists annotated ground truth pathologies using AI-assisted labeling tools
Phase 2: Model Training & Tuning
- Trained foundational models on large-scale open and proprietary datasets
- Fine-tuned models on site-specific scans to handle scanner artifacts and patient mix
Phase 3: Workflow Integration
- Integrated with PACS/RIS systems via DICOM routing and HL7/FHIR APIs
- Deployed triage and report modules through on-prem servers and cloud instances
Phase 4: Pilot Deployment & Feedback
- Piloted across 10 centres with radiologist-in-the-loop validation
- Implemented continuous model monitoring and retraining using post-deployment feedback
Business Impact
- 92% Sensitivity and >90% Specificity in detecting target pathologies
- 50% Reduction in turnaround time for critical scans
- 4x Faster triage for emergency radiology workflows
- Improved radiologist satisfaction with reduced burnout and focus on complex reads
Strategic Significance
Beyond operational efficiency, the AI solution enabled strategic gains:
- Improved Clinical Outcomes by reducing missed or late diagnoses
- Regulatory Compliance with SLA adherence for emergency reads
- Scalable Infrastructure to extend radiology services to remote and underserved areas
- Generated Proprietary AI-augmented Reports for medicolegal and audit usage
The deployment demonstrated how AI can be seamlessly embedded into clinical radiology workflows to assist, not replace, expert judgment.
Looking Ahead
Following the initial rollout, the client is now exploring:
- AI-Driven Screening Programs for tuberculosis, breast cancer (mammography), and lung nodules
- Automatic AI based Report Generation for radiologists to review and sign out
- Multi-modal Prognostic Models integrating imaging with lab and EMR data
- Auto-Quantification Tools (e.g., cardiac ejection fraction, tumor volume)
- Real-time Radiologist Co-Pilot with voice-to-report and structured templates
- Integration into Public Health Systems for population screening initiatives
The foundation models are also being adapted for cross-modality workflows including ultrasound and PET.
Key Takeaways
This use case shows how radiology departments and diagnostic networks can:
- Accelerate turnaround time and quality using GenAI-based image understanding
- Enable scalable radiology services across geographies without compromising accuracy
- Establish data-driven, AI-first workflows for diagnostics and remote care
Let’s Transform Your Radiology Workflows
Let us help your imaging center or hospital network deploy trustworthy, scalable, and explainable AI systems. Reach out to explore how foundation models and federated learning can power the next generation of radiology services.