Systematic literature review on the application of explainable artificial intelligence in palliative care studies.

Journal: International journal of medical informatics
PMID: 40250167

Abstract

BACKGROUND: As machine learning models become increasingly prevalent in palliative care, explainability has become a critical factor in their successful deployment in this sensitive field, where decisions can profoundly impact patient health and quality of life. To address these concerns, Explainable AI (XAI) aims to make complex AI models more understandable and trustworthy.

Authors

  • Battushig Migiddorj
    Faculty of Engineering and Information Science, University of Wollongong, Australia. Electronic address: bm481@uowmail.edu.au.
  • Marijka Batterham
    Faculty of Engineering and Information Science, University of Wollongong, Australia.
  • Khin Than Win
    School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2500, Australia.

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