QAS.AI was selected to pitch at the inaugural MedTech Innovator Radar Forum in Manhattan Beach, CA, joining the top 10% of more than 1,800 applicants invited to present to investors, strategic partners, and industry leaders.

QAS.AI was selected as a first-cohort awardee of the Polyphonic™ AI Fund for Surgery QuickFire Challenge, a global initiative from Johnson & Johnson MedTech advancing surgical AI.

QAS.AI's innovative approach to enhancing aneurysm surgery through artificial intelligence has been highlighted in a recent article by WNY Papers. The piece discusses our collaborative efforts with the University at Buffalo to develop AI-driven tools that assist neurosurgeons in predicting the success of aneurysm treatments. By analyzing angiographic imaging data, our technology aims to provide real-time insights during procedures, potentially improving patient outcomes and surgical precision.​

QAS.AI has received a research and development grant from the University at Buffalo's Center for Advanced Technology in Big Data and Health Sciences (UB CAT). This funding supports our ongoing collaboration with UB neurosurgery faculty to enhance our AI platform's capabilities in predicting aneurysm healing during neuroendovascular procedures. The grant facilitates the refinement of our algorithms and the expansion of clinical validation efforts, aiming to improve patient outcomes through real-time, data-driven decision support.​

We’re excited to share that QAS.AI has received a $1 million Phase II grant from the National Science Foundation (NSF) through the Small Business Technology Transfer (STTR) program. This award will support the development of our AI-powered platform for real-time, intraoperative prognosis of aneurysm occlusion. The project moves beyond prototype, enabling clinical-grade software development and multi-site evaluation in collaboration with Gates Vascular Institute, Mercy Hospital of Buffalo, and the University of South Florida. Our goal: empower clinicians with automated occlusion probability insights during neuroendovascular procedures, reducing the need for follow-up interventions and enhancing patient outcomes.