Transforming Stroke Diagnosis with Artificial Intelligence: A Scoping Review of Brainomix e-Stroke, Aidoc, RapidAI, and Viz.ai

TL;DR

This paper reviews AI tools for stroke diagnosis, highlighting their accuracy, workflow benefits, and cost-effectiveness in improving stroke care.

Contribution

The study provides a comprehensive scoping review of four AI platforms, comparing their diagnostic performance and impact on stroke care.

Findings

AI platforms show high sensitivity for proximal large vessel occlusions but variable performance for distal and posterior circulation occlusions.

RapidAI is often used with historical perfusion parameters, but its outputs differ from platforms like Viz.ai.

Viz.ai is linked to reduced door-to-puncture times, and Brainomix is well-validated for automated NCCT ASPECTS triage.

Abstract

Background and Objectives: Rapid diagnosis is fundamental to acute ischemic stroke management; however, access to neuroradiological expertise remains limited. This scoping review maps the diagnostic accuracy, workflow impact, and cost-effectiveness of leading AI platforms (Brainomix, Aidoc, RapidAI, and Viz.ai), characterizing industry and peer-reviewed metrics. Materials and Methods: Following PRISMA-ScR guidelines, we searched PubMed, Cochrane Library, and HTA repositories for studies (2019–2025). Using a PICO-based framework, 29 studies were included for thematic mapping of the technological landscape. Results: Twenty-nine studies were included. Platforms show high proximal LVO sensitivity (78–97%), while performance for distal/MVO and posterior circulation occlusions was more variable. RapidAI is frequently mapped using historical perfusion trial parameters; however, volumetric discrepancies with platforms like Viz.ai indicate outputs are not interchangeable. Brainomix shows extensive validation for automated NCCT ASPECTS in triage. Aidoc demonstrates operational advantages via worklist prioritization, while. Viz.ai is associated with door-to-puncture time reductions (11–25 min). Economically, cost-effectiveness is driven by improved functional outcomes and expanded access to thrombectomy, rather than labor substitution. Conclusions: AI platforms function as diagnostic safety nets and workflow optimizers. Reported roles, such as perfusion-centric analysis (RapidAI) or workflow coordination (Viz.ai), reflect current research trends rather than definitive technological superiority. Institutional selection should consider these evidence clusters alongside local validation and specific clinical priorities.

Figures