Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Pulsed-field ablation versus thermal ablation in the management of atrial fibrillation: A narrative review.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing·2026
Same author

Clinical and Genetic Spectrum of Filippi Syndrome: A Systematic Review of Published Case Reports and Case Series.

Health science reports·2026
Same author

Validation of the immune-mediated colitis endoscopic score using a retrospective cohort of patients with immune-mediated colitis.

iGIE : innovation, investigation and insights·2026
Same author

Evaluating and Mitigating Carbon Dioxide Equivalent Emissions in Stroke Management: A Modeling Study.

Journal of the American Heart Association·2026
Same author

Timing of Antithrombotic Therapy Among Patients With Hemorrhagic Transformation After an Ischemic Stroke.

Journal of the American Heart Association·2026
Same author

Bioprospection of Metschnikowia species as a biocontrol agent against AFB<sub>1</sub> contamination.

Scientific reports·2026

Related Experiment Video

Updated: Dec 9, 2025

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.5K

Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework.

Vida Abedi1, Ayesha Khan2, Durgesh Chaudhary2

  • 1Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA.

Therapeutic Advances in Neurological Disorders
|September 14, 2020
PubMed
Summary

Artificial intelligence (AI) can aid in faster stroke diagnosis by analyzing patient data. This framework aims to reduce treatment delays and improve outcomes for stroke patients.

Keywords:
acute strokeartificial intelligencecerebrovascular disease/strokecomputer aided diagnosisischemic strokemachine learningstroke diagnosisstroke in emergency department

More Related Videos

Prehospital Thrombolysis: A Manual from Berlin
05:52

Prehospital Thrombolysis: A Manual from Berlin

Published on: November 26, 2013

22.2K
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.4K

Related Experiment Videos

Last Updated: Dec 9, 2025

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.5K
Prehospital Thrombolysis: A Manual from Berlin
05:52

Prehospital Thrombolysis: A Manual from Berlin

Published on: November 26, 2013

22.2K
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.4K

Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Stroke is a leading cause of death and disability globally.
  • Timely intervention is critical for stroke patients due to a narrow therapeutic window.
  • Current methods for recognizing stroke signs in acute settings present challenges, leading to delayed treatment.

Purpose of the Study:

  • To present a practical framework for developing an AI-powered decision support system for stroke diagnosis.
  • To improve emergency department providers' ability to diagnose stroke accurately and swiftly.
  • To reduce treatment delays and enhance patient outcomes in acute stroke care.

Main Methods:

  • Utilizing artificial intelligence (AI) and clinical data from electronic health records.
  • Integrating patient-presenting symptoms into the AI model.
  • Developing a practical framework reflecting various stages of system development.

Main Results:

  • The proposed framework facilitates the creation of an AI decision support system for stroke.
  • The system aims to assist emergency department providers in diagnosing stroke.
  • Implementation can potentially reduce critical treatment delays.

Conclusions:

  • AI-driven decision support systems offer a promising approach to improve acute stroke diagnosis.
  • Addressing technical, operational, and ethical challenges is crucial for successful implementation.
  • This framework provides a pathway to enhance patient care and outcomes for stroke.