An Explainable AI Application (AF'fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study.

Journal: JMIR human factors
PMID: 39946707

Abstract

BACKGROUND: The opaque nature of artificial intelligence (AI) algorithms has led to distrust in medical contexts, particularly in the treatment and monitoring of atrial fibrillation. Although previous studies in explainable AI have demonstrated potential to address this issue, they often focus solely on electrocardiography graphs and lack real-world field insights.

Authors

  • Wan Jou She
    Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto, Japan.
  • Panote Siriaraya
    Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto, Japan.
  • Hibiki Iwakoshi
    Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
  • Noriaki Kuwahara
    Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto, Japan.
  • Keitaro Senoo
    Department of Cardiac Arrhythmia Research and Innovation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.

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