Evaluating the impact of explainable AI on clinicians' decision-making: A study on ICU length of stay prediction.

Journal: International journal of medical informatics
Published Date: Apr 21, 2025

Abstract

BACKGROUND: Explainable Artificial Intelligence (XAI) is increasingly vital in healthcare, where clinicians need to understand and trust AI-generated recommendations. However, the impact of AI model explanations on clinical decision-making remains insufficiently explored.

Authors

  • Jinsun Jung
    College of Nursing, Seoul National University, Seoul, Republic of Korea; Center for Human-Caring Nurse Leaders for the Future by Brain Korea 21 (BK 21) Four Project, College of Nursing, Seoul National University, Seoul, Republic of Korea.
  • Sunghoon Kang
    The Department of Science Studies, Seoul National University, Seoul, Republic of Korea.
  • Jeeyae Choi
    School of Nursing, University of North Carolina Wilmington, NC, USA.
  • Robert El-Kareh
    Division of Biomedical Informatics, UCSD, San Diego, CA, USA.
  • Hyungbok Lee
    College of Nursing, Seoul National University, Seoul, Republic of Korea; Seoul National University Hospital, Seoul, Republic of Korea.
  • Hyeoneui Kim
    Department of Biomedical Informatics, School of Medicine, UC, San Diego, CA 92093, USA.

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