AI-Assisted Diagnostic System for Healthcare Imaging
A multi-specialty hospital chain aimed to improve diagnostic accuracy and speed for radiology and pathology workflows using AI. Their objective was to support doctors with faster, consistent, and explainable image analysis.
The Challenge
Complexities We Had to Tackle
- High diagnostic load and shortage of radiologists.
- Variability in interpretations leading to inconsistent reports.
- Long turnaround times for X-ray, CT, and MRI analyses.
The Solution
Turning Challenges into Capabilities
Cognoverse developed an AI-assisted diagnostic system that leverages deep getting to know models for photo classification, segmentation, and anomaly detection across multiple imaging modalities.
Key Features:
- Multi-modal Imaging AI: CNN-based models trained on labeled X-ray, CT, and MRI datasets for disease prediction.
- Explainable AI Interface: Heatmaps and visual highlights to support doctors’ decision-making.
- Cloud Integration: Secure DICOM storage with HL7 FHIR interoperability.
- Workflow Automation: Automated file technology and case prioritization based on risk scoring.
Impact
The AI-assisted diagnostic platform empowered healthcare experts to supply quicker more correct diagnoses main to better affected person results and improved operational performance.
Technology Stack
- Python, PyTorch, MONAI framework
- AWS Health Lake + DICOM integration
- React dashboard for clinician interface
- RESTful API for EHR system connectivity
