Using Artificial Intelligence for Improving Stroke Diagnosis in Emergency Departments: a Practical Framework

Overview

Journal Ther Adv Neurol Disord

Specialty Neurology

Date 2020 Sep 14

PMID 32922515

Citations 18

Authors

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

Affiliations

Soon will be listed here.

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.

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