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This paper is in the following e-collection/theme issue:

User-Centered Design Case Studies (740) Clinical Information and Decision Making (2070) Usability of Apps and User Perceptions of mHealth (1240) Artificial Intelligence (2347) Focus Groups and Qualitative Research for Human Factors Research (1304) Design and Usability of Clinical Software and EHRs (368) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1810) Machine Learning (2229) User Needs and Competencies (492) Cardiac Arrhythmia, Atrial Fibrillation (116)

Published on in Vol 12 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65923, first published .
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

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

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

Authors of this article:

Wan Jou She1 Author Orcid Image ;   Panote Siriaraya1 Author Orcid Image ;   Hibiki Iwakoshi2, 3 Author Orcid Image ;   Noriaki Kuwahara1, 4 Author Orcid Image ;   Keitaro Senoo2, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Makino M, She W, Siriaraya P, Matoba S, Senoo K. Evaluating the appropriateness and safety of generative AI in delivering lifestyle guidance for atrial fibrillation patients. Scientific Reports 2025;16(1) View
  2. Andritsch J, Dike J. XAI -Driven Explainability for Cardiovascular Diseases Prediction. Journal of Informatics and Web Engineering 2026;1(5):167 View