MedPulsar brings deep learning to radiology, delivering faster and more accurate reads for hospitals and imaging centers worldwide.
Six pillars that define the MedPulsar platform and set it apart in clinical AI imaging.
Deep learning models trained on millions of annotated scans deliver diagnostic insights in real time.
High-resolution visualization layers highlight anomalies and structures with pixel-level accuracy.
Clinical validation across MRI, CT, and X-ray modalities with 97.4% reported accuracy on test sets.
Live dashboards track throughput, flagged cases, and radiologist agreement rates across your facility.
Sub-second inference on standard radiology hardware means no pipeline bottlenecks during peak hours.
Tested in peer-reviewed trials at three teaching hospitals in Japan and South Korea.
MedPulsar integrates directly with your PACS workflow, automating pre-reads before the radiologist even opens the case.
DICOM files are received from your PACS or modality via secure HL7 FHIR-compatible channels.
Specialized models for each modality run in parallel, generating structured findings reports automatically.
Findings are surfaced inside your existing viewer. The radiologist validates, amends, and signs off.
Research, clinical case studies, and perspectives on the future of AI in radiology.
A comprehensive guide to implementing AI-assisted diagnostics in modern radiology departments.
How convolutional neural networks are improving diagnostic accuracy across major imaging modalities.
Comparing AI performance benchmarks across MRI and CT scan datasets from clinical trial settings.