Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework. (August 2020)

• Record Type: Journal Article
• Authors: Abedi, Vida; Khan, Ayesha; Chaudhary, Durgesh; Misra, Debdipto; Avula, Venkatesh; Mathrawala, Dhruv; Kraus, Chadd; Marshall, Kyle A.; Chaudhary, Nayan; Li, Xiao; Schirmer, Clemens M.; Scalzo, Fabien; Li, Jiang; Zand, Ramin
• Is Part Of: Therapeutic advances in neurological disorders. Volume 13(2020)
• Publication Date: 2020-08
• Subjects: acute stroke -- artificial intelligence -- cerebrovascular disease/stroke -- computer aided diagnosis -- ischemic stroke -- machine learning -- stroke diagnosis -- stroke in emergency department; Nervous system -- Diseases -- Periodicals; Nervous system -- Degeneration -- Periodicals; Nervous system -- Diseases -- Treatment -- Periodicals; Nervous System Diseases -- therapy -- Periodicals; Neurodegenerative Diseases -- Periodicals; Système nerveux -- Maladies -- Périodiques; Système nerveux -- Dégénérescence -- Périodiques; Système nerveux; Système nerveux -- Maladies -- Traitement -- Périodiques; 616.805
• DOI: 10.1177/1756286420938962
• Languages: English
• ISSNs: 1756-2856

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.