The Role of Artificial Intelligence in Acute Stroke Imaging: Current Status and Future Directions
Bibliographic record
Abstract
Acute stroke imaging is paramount for quick diagnostic decisions using imaging modalities, including computerized tomography and magnetic resonance imaging. These modalities provide valuable information to determine the disease etiology and course of action. "Time is brain," and as such, there are unique challenges regarding acute stroke imaging, including acute decision making, limited resource availability, and compromised image quality. Artificial intelligence (AI) tools may provide support for acute stroke imaging in clinical care settings and have the potential to mitigate many of these challenges. This paper investigates the role of AI in acute stroke imaging in research and industry, including reviewing 39 papers published between 2022 and 2025 that investigated the development and application of tools specific to interventional radiology and stroke and examined their tools' efficacy and the studies' consideration of data privacy, reproducibility, and practical usability. We also investigated four commercially available AI tools available for clinical use, focusing on their primary objectives, strengths, and limitations. We found that while AI tools demonstrate the potential for improving time-to-treatment and diagnostic accuracy, there are key limitations related to low reproducibility, the development of impractical tools, and minimal documentation about AI development and employment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".