Artificial Intelligence and Novel Trial Designs for Acute Ischemic Stroke: Opportunities and Challenges
Bibliographic record
Abstract
The Stroke Treatment Academic Industry Roundtable convened a workshop regarding artificial intelligence (AI) and innovative clinical trial designs during the Stroke Treatment Academic Industry Roundtable XIII meeting on March 28, 2025. This forum brought together stroke physicians and researchers, and industry representatives to discuss the current use and future opportunities for AI and novel trial designs in acute stroke trials. AI already plays a substantial role in the treatment of acute stroke with regards to imaging but is poised to have a much larger impact in clinical care and research trials over the coming years. The quality and understanding of the data are used to train the AI, the human element needed to ensure training is successful, and the clinician and trialist at the bedside, the humans "in the loop," will be necessary to maximize AI's effectiveness in clinical practice and trials. Platform trials address multiple scientific questions in an area of medicine simultaneously within the same trial structure by sharing controls across multiple interventions. While platform trials increase efficiency and potentially decrease the time needed to answer important clinical scientific questions, they also can introduce complexity to standard workflows. Future acute ischemic stroke clinical trials should incorporate elements of pragmatic and patient-centered trial design when possible. Pragmatic trials aim to assess the effectiveness of treatments when they are implemented into routine clinical care rather than under idealized conditions. AI models and platform, pragmatic, and patient-centered trial designs are new tools to answer important clinical questions, but understanding how they work, their best uses, and their limitations is critical for accelerating successful new treatments for stroke.
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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 | 0.573 | 0.595 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".