Effects of explainable artificial intelligence in neurology decision support.

Journal: Annals of clinical and translational neurology
Published Date: Apr 5, 2024

Abstract

OBJECTIVE: Artificial intelligence (AI)-based decision support systems (DSS) are utilized in medicine but underlying decision-making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI-based DSS in decision-making tasks as compared to a general population.

Authors

  • Grace Y Gombolay
    Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia. Electronic address: ggombol@emory.edu.
  • Andrew Silva
    Georgia Institute of Technology, Atlanta, GA, USA.
  • Mariah Schrum
    Georgia Institute of Technology, Atlanta, GA, USA.
  • Nakul Gopalan
    Georgia Institute of Technology, Interactive Computing, Atlanta, Georgia.
  • Jamika Hallman-Cooper
    Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia.
  • Monideep Dutt
    Department of Pediatrics, Division of Neurology, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA.
  • Matthew Gombolay
    Department of Pediatrics, Division of Neurology, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA.

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