Medical Image Analysis with AI

Rigorous Clinical Validation

Our models undergo extensive validation following FDA and CE-mark regulatory pathways. This includes retrospective validation on large diverse datasets, prospective clinical trials, and ongoing performance monitoring in real-world deployments.

We report performance using clinical metrics (sensitivity, specificity, AUC) with confidence intervals, and compare against expert radiologist performance. All algorithms are tested across different demographics, scanner types, and imaging protocols to ensure generalizability.

Explainable AI for Clinical Trust

Radiologists need to understand AI recommendations to trust them. Our systems provide visual explanation maps (saliency maps, attention visualizations) showing exactly which image regions influenced the AI's decision.

We also provide quantitative measurements, comparison to normal ranges, and references to similar cases in the training dataset. This transparency helps clinicians verify AI findings and use them confidently in clinical decision-making.

Seamless Clinical Integration

Our AI integrates directly with PACS, RIS, and radiology reporting systems via DICOM, HL7, and FHIR standards. Images are processed automatically upon acquisition, with results displayed in radiologists' existing workflows - no separate applications needed.

AI findings can be incorporated into structured reports, annotations overlaid on images, and critical alerts sent to clinical teams. The system tracks which AI recommendations were accepted or modified, creating a feedback loop for continuous improvement.

Is AI replacing radiologists?

No. Our AI is designed to assist, not replace, radiologists. Think of it as an intelligent second opinion that helps radiologists work faster and more accurately. The radiologist remains in control of all clinical decisions and final reports. AI handles the tedious tasks (identifying potential abnormalities, measurements, comparisons to priors) while radiologists apply clinical judgment, integrate with patient history, and make final diagnostic decisions.

How accurate is medical imaging AI compared to radiologists?

For specific tasks, state-of-the-art AI can match or exceed average radiologist performance. However, AI excels at pattern recognition in specific domains it's trained for, while radiologists have broader clinical knowledge and can handle edge cases. The best performance comes from AI-assisted radiologists - studies show radiologists using AI outperform either alone. Typical improvements are 5-10% higher sensitivity with maintained specificity.

What regulatory approvals are needed for medical imaging AI?

In the US, most medical imaging AI requires FDA clearance or approval (Class II or III medical devices). In Europe, CE marking under the Medical Device Regulation (MDR) is required. We help navigate this process, providing regulatory documentation, clinical validation data, and quality management systems. Some AI tools qualify for lower regulatory pathways if used purely for workflow optimization without diagnostic claims.

How do you ensure AI works on our specific scanner and protocols?

We validate and fine-tune models for your specific imaging equipment, protocols, and patient population. This involves testing on your historical data, adjusting for differences in image quality, field of view, and technical parameters. We support all major vendors (GE, Siemens, Philips, Canon) and can adapt to custom protocols. Performance monitoring continues after deployment to ensure ongoing accuracy.

What's the implementation timeline and cost structure?

Implementation typically takes 8-16 weeks: regulatory review, technical integration with PACS/RIS, validation on your data, radiologist training, and go-live support. Pricing models include per-study fees (typical for screening applications), per-seat licenses, or annual platform fees. Many health systems see ROI within 12-18 months from increased capacity, faster turnaround, and reduced missed findings. We offer pilot programs to demonstrate value before full deployment.