# Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review

## TL;DR

This paper reviews how artificial intelligence is currently being used in stroke care, from pre-hospital to recovery, and highlights the lack of strong clinical evidence for its effectiveness.

## Contribution

The paper provides a comprehensive literature review on the clinical applications of AI in stroke care, emphasizing the current evidence levels and challenges.

## Key findings

- No randomized controlled trials (RCTs) currently evaluate AI's impact on patient outcomes in stroke care.
- AI has commercial applications in acute stroke care, particularly in imaging detection and telestroke assistance.
- Early RCTs support AI's use in recovery phase technologies like robotics and brain-computer interfaces.

## Abstract

Background: Artificial intelligence (AI) has emerged as a transformative tool in medicine, leveraging rapid analysis of large datasets to accelerate diagnosis, enhance clinical decision-making, and improve clinical workflows. This is highly relevant in stroke care given the time-sensitive nature of the disease process. This review evaluates the current landscape of evidence-based medicine utilizing AI in stroke, with emphasis on its use in phases of clinical care across the stroke continuum, including pre-hospital, acute, and recovery phases. This offers a comprehensive understanding of the current state of AI in both practice and literature. Methods: A review of major databases was conducted, identifying peer-reviewed literature evaluating the use of AI and its level of evidence across the stroke continuum. Given the heterogeneity of study designs, interventions, and outcome metrics spanning multiple disciplines, findings were synthesized narratively. Results: Across all phases of care, there remain no randomized controlled trials (RCTs) evaluating patient-level outcome data using AI (Level A). In the pre-hospital phase of care, AI has been used to identify stroke symptoms and assist EMS routing/training but presently remains limited to research. AI is most studied in the acute phase of care, representing the only phase to achieve commercial application in imaging detection and telestroke assistance, supported by non-randomized evidence (Level B-NR). In the recovery phase, AI may enhance wearable technologies, tele-rehabilitation, and robotics/brain–computer interfaces, with early RCTs (Level B-R) supporting the latter two, representing the strongest evidence for AI in stroke care to date. Conclusions: Despite the potential for AI to transform all phases of care across the stroke continuum, major challenges remain, including transparency, generalizability, equity, and the need for externally validated clinical studies.