Radiation oncology patients' perceptions of artificial intelligence and machine learning in cancer care: A multi-centre cross-sectional study.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Apr 13, 2025

Abstract

AIM: The use of artificial intelligence (AI) and machine learning (ML) is increasingly widespread in radiation oncology. However, patient engagement to date has been poor. Respect for persons in the healthcare setting and the principle of informed consent requires recognition of patient perspectives. The aim of this study was to provide a baseline understanding of patient views about the use of AI/ML in the specific context of radiotherapy to contribute towards future governance of the technology.

Authors

  • Joseph Chan
    Department of Radiation Oncology, Royal North Shore Hospital, Sydney, New South Wales, Australia; Kolling Institute of Medical Research, The Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia. Electronic address: Joseph.chan@health.nsw.gov.au.
  • Lisa Parker
    Department of Radiation Oncology, Royal North Shore Hospital, Sydney, New South Wales, Australia; Northern Clinical School, University of Sydney, Sydney, New South Wales, Australia. Electronic address: Lisa.parker@health.nsw.gov.au.
  • Stacy Carter
    Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, University of Wollongong, Wollongong, New South Wales, Australia.
  • Brooke Nickel
    Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
  • Susan Carroll
    Department of Radiation Oncology, Royal North Shore Hospital, Sydney, New South Wales, Australia; Northern Clinical School, University of Sydney, Sydney, New South Wales, Australia; Western Cancer Centre Dubbo, Dubbo Base Hospital, New South Wales, Australia. Electronic address: Susan.carroll@health.nsw.gov.au.

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