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Artificial Intelligence and Novel Trial Designs for Acute Ischemic Stroke: Opportunities and Challenges

  • Joseph P. Broderick*
  • , Eva A. Mistry
  • , Paul M. Wechsler
  • , Mitchell S.V. Elkind
  • , David S. Liebeskind
  • , George Harston
  • , Jake Wolenberg
  • , Jennifer A. Frontera
  • , W. Taylor Kimberly
  • , Christopher G. Favilla
  • , Johannes Boltze
  • , Johanna Ospel
  • , Edgar A. Samaniego
  • , Opeolu Adeoye
  • , Scott E. Kasner
  • , Lee H. Schwamm
  • , Gregory W. Albers
  • , Oscar Bolanos
  • , Bruce C.V. Campbell
  • , Davide Carone
  • Emir Deljkich, A. Rada Eltatawy, Marc Fisher, Jeremy Heit, Michael D. Hill, Gary Houser, Edward C. Jauch, Hooman Kamel, Del Kjos, Sara Ortiz, Sean I. Savitz, Kevin N. Sheth, Michael Tymianski, Ajay K. Wakhloo
*Corresponding author for this work
  • University of Cincinnati
  • American Heart Association
  • David Geffen School of Medicine at UCLA
  • Oxford University Hospitals NHS Foundation Trust
  • Imperative Care
  • New York University School of Medicine
  • Massachusetts General Hospital
  • University of Pennsylvania
  • University of Warwick
  • University of Calgary
  • University of Iowa Carver College of Medicine
  • Washington University St. Louis
  • Yale University
  • Stanford University

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish
Pages (from-to)265-274
Number of pages10
JournalStroke
Volume57
Issue number1
DOIs
StatePublished - Jan 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • artificial intelligence
  • atrial fibrillation
  • hemorrhage
  • infarction
  • ischemic stroke

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