We elaborate optimized custom solutions for doctors and surgeons addressing market needs: through a unique integration approach, we combine innovative digital technologies into a phygital platform to improve safety in medical practice
HUVANT is an AI-driven simulation platform that generates patient-specific 3D digital twins with critical reference points to support surgical planning and improve clinical safety.
A patient centred solution to explore, plan and test procedures before the OR
Proven realism, repeatable simulation
Cadaver-based training represents the gold standard for anatomical accuracy, but it is limited in availability, scalability, and ethical sustainability. Our approach builds on that benchmark by enabling realistic and repeatable training in a controlled environment.
Artificial Intelligence
To process medical imaging data and
generate digital twins with critical landmarks (optimal access site, the path to follow, and the final target zone)
Virtual Reality
To visualize and interact with digital 3D models into real clinical settings
3D Digital Fabrication
The experience is then completed by the production of physical twins with superior tactile feedback for practical surgical rehearsal.
A global problem
Every year, over 138 million patients suffer harm from medical errors; 15% of global healthcare spending ($1.5 trillion) is attributable to malpractice.
Unmet need
Healthcare professionals need advanced simulation and navigation tools to plan personalized surgical procedures, reducing clinical errors
Clinical practice is facing a clear need for innovative, safe and scalable solutions capable of supporting pre-operative and intra-operative planning, surgical navigation, and AI-assisted decision support.
3
Medical errors are the third leading cause of death in the US
10
Becoming an indipendent surgeon is slow and costly, requiring 7-10 years of training
Our impact
Reduce errors before they reach the OR
Improve outcomes while lowering costs
Build a proprietary 3D anatomy data asset
Patient-specific anatomical landmark mapping
Long read
