Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development.

Journal: Radiography (London, England : 1995)
PMID: 39454460

Abstract

OBJECTIVES: Artificial intelligence (AI) is rapidly being integrated into medical imaging practice, prompting calls to enhance AI education in undergraduate radiography programs. Combining evidence from literature, practitioner insights, and industry perspectives, this paper provides recommendations for medical imaging undergraduate education, including curriculum revision and re-alignment.

Authors

  • E Crotty
    Queensland University of Technology, School of Clinical Sciences, Faculty of Health, Brisbane, QLD, Australia.
  • A Singh
    Department of Otorhinolaryngology and Head and Neck Surgery, All India Institute of Medical Sciences, Room no. 4057, ENT Office, 4th floor, Teaching Block, Ansari Nagar, New Delhi, 110029 India.
  • N Neligan
    Queensland University of Technology, School of Clinical Sciences, Faculty of Health, Brisbane, QLD, Australia.
  • C Chamunyonga
    Queensland University of Technology, School of Clinical Sciences, Faculty of Health, Brisbane, QLD, Australia.
  • C Edwards
    School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.

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