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

Vida Abedi, Ayesha Khan, Durgesh Chaudhary, Debdipto Misra, Venkatesh Avula, Dhruv Mathrawala, Chadd Kraus, Kyle A. Marshall, Nayan Chaudhary, Xiao Li, Clemens M. Schirmer, Fabien Scalzo, Jiang Li, Ramin Zand

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

Abstract

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients’ presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.

Original languageEnglish (US)
JournalTherapeutic Advances in Neurological Disorders
Volume13
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Neurology
  • Clinical Neurology

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