Discover the Power of
AI Driven Hybrid Solutions
Models have been comprehensively trained and validated on heterogeneous datasets covering emergency thoracic CT and abdominal CT scans.
AI Tech in Health
Redefining
Medical Image Analysis
Reduced Interpretation Time ⋅ Optimized Patient Management ⋅ Enhanced Radiology Workflow
AortaSense Pro™
Automated AI interpretation for aortic CT images, improving efficiency and reliability.
Project No: 7230344
AbdoXpert Suite™
Comprehensive AI-driven diagnostic suite for abdominal imaging, optimizing detection and analysis across multiple organs.
Project No: 7251157
Integration
Choose the integration method that fits your workflow.
TUBITAK 1507 - 2023
AortaSense Pro - Project No: 7230344
Siemens Healthineers
Future of Imaging - 2024
Winner
Siemens Healthineers
SHIFT Day - 2025
Finalist
Workup AStart - 2025
Acceleration Program Graduation
TUBITAK 1507 - 2025
AbdoXpert Suite - Project No: 7251157
Showreel
Behind the scenes of Healysense: innovation, expertise, and vision.
They combine expertise in radiology, artificial intelligence, and mathematics to develop revolutionary hybrid decision support systems.
Join our journey to redefine medical analysis and meet our team.
What clinical challenges does address?
Healysense is designed to assist hospital networks and radiologists by leveraging AI to enhance both the efficiency and accuracy of medical image interpretation. Its outputs are intended to complement the clinician’s expertise—not replace it. The final interpretation and diagnosis always remain the responsibility of the healthcare professional.
How are results displayed?
Results are displayed through our intuitive interface with clear visualizations, confidence scores, and detailed annotations that help clinicians make informed decisions quickly and accurately.
Giray Nuri Mavis, Berkay Ahmet Durmus, Semih Burhan, and Serhat Tozburun "Wavelet-informed pix2pix model with an FID-based loss function for confocal microscopy", Proc. SPIE 13937, Advances in Microscopic Imaging V, 139371B (18 December 2025); https://doi.org/10.1117/12.3098091
2.Giray Nuri Mavis, Berkay Ahmet Durmus, Semih Burhan, and Serhat Tozburun "Wavelet-informed pix2pix model with an FID-based loss function for confocal microscopy", National Workshop on Optics, Electro-Optics and Photonics, İstanbul, Türkiye 2025 (12 September 2025)
3.Giray Nuri Mavis, Berkay Ahmet Durmus, Semih Burhan, Dilek Nazli, Gunes Ozhan, and Serhat Tozburun "Conditional generative adversarial network-based image enhancement model for confocal microscopy", Proc. SPIE 13865, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences III, 138650C (5 March 2026); https://doi.org/10.1117/12.3079178
Ready to Integrate Our AI Solutions Into Your Workflow
Start using the power of artificial intelligence to enrich your radiologic workflow.