RapidAI has announced that it had a total of 28 scientific abstracts accepted and presented at the 2026 International Stroke Conference (ISC; 4–6 February, New Orleans, USA), spanning aneurysm monitoring, ischaemic stroke detection, advanced imaging visualisation, and radiology workflow optimisation. Findings from the abstracts reflect the impact of deep clinical artificial intelligence (AI) delivered through the Rapid Enterprise platform, designed to scale across radiology and acute workflows, as stated in a company press release.
The clinical data demonstrate how RapidAI’s clinically validated, US Food and Drug Administration (FDA)-cleared AI solutions can improve diagnostic performance, reduce interpretation time, and deliver measurable efficiency and economic gains for stroke teams and radiology departments, the release continues, adding that the company’s unique approach—deep clinical AI—delivers “real results” by providing visualisation, localisation, characterisation, and tracking changes over time, as well as triage notifications.
One of these abstracts saw researchers evaluate longitudinal aneurysm measurements using RapidAI’s aneurysm platform and compare them with neuroradiologists’ interpretations. Rapid detected 46% more clinically significant growth compared with neuroradiologists alone—with 27 out of 28 incidents of aneurysm growth detected by Rapid versus 14 out of 28 for neuroradiologists alone. The company claims these findings indicate that its aneurysm platform reliably captures true linear growth while maintaining comparable specificity, supporting improved longitudinal monitoring and assessment of rupture risk, potentially enabling earlier, life-saving interventions.
“Identifying true aneurysm growth over time is challenging, even for highly experienced neuroradiologists,” said lead author Jeremy Heit (Stanford University, Stanford, USA). “Our findings suggest that deep clinical AI can serve as a valuable adjunct by consistently detecting subtle changes that are difficult to discern visually, helping support longitudinal assessment and more informed decision-making aimed at preventing aneurysm rupture and stroke.”
Additionally, a multi-reader, multi-case study assessed the impact of Lumina 3D—Rapid’s automated 3D head-and-neck reconstructions solution—on stroke occlusion and stenosis detection, as well as interpretation time. Across 20 cases involving 24 occlusions and 11 stenoses, diagnostic accuracy improved from 76.1% without Lumina 3D to 85.6% with Lumina 3D, representing a 9% improvement (p=0.0004). Interpretation time decreased by an average of 34 seconds per case (p<0.05), with general radiologists achieving time savings of more than one minute per case.
RapidAI thus states that the study shows Lumina 3D can enhance stroke workflows by enabling faster and more accurate identification of clinically significant stroke findings, potentially enabling faster interventions and better outcomes for stroke patients.
Based on the fact that—owing to a shortage of computed tomography (CT) technologists across the USA—time savings and efficiency are increasingly important in ensuring diagnostic centres keep up with the growth in imaging demands, another abstract saw researchers assess the operational performance of CT technologists before and after the implementation of Lumina 3D.
Prior to deployment, manual reconstructions averaged 31 minutes per patient. Following implementation, automated reconstructions required seven minutes, resulting in a 77.4% reduction in technologist time, or 24 minutes saved per image. Based on monthly CT angiography volumes, the time savings translated to approximately 81.6 hours saved per month, enabling 108 additional scans and generating an estimated US$43,000 in additional monthly imaging revenue. The results demonstrate how AI-driven automation can expand imaging capacity while reducing staff workload, according to RapidAI.
“These ISC abstracts reflect the depth and rigor of the clinical evidence supporting our unique solutions,” commented Karim Karti, chief executive officer (CEO) of RapidAI. “Together, these studies demonstrate the power of deep clinical AI when it is seamlessly integrated across radiology and care team workflows, and built on an enterprise-grade platform. Proof matters in AI, and these data show meaningful gains in diagnostic confidence and operational efficiency with RapidAI.”
