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The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement.
Chamunyonga, Crispen; Edwards, Christopher; Caldwell, Peter; Rutledge, Peta; Burbery, Julie.
Afiliación
  • Chamunyonga C; School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia. Electronic address: crispen.chamunyonga@qut.edu.au.
  • Edwards C; School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
  • Caldwell P; School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
  • Rutledge P; School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
  • Burbery J; School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
J Med Imaging Radiat Sci ; 51(2): 214-220, 2020 06.
Article en En | MEDLINE | ID: mdl-32115386
ABSTRACT
Artificial intelligence (AI) and machine learning (ML) approaches have caught the attention of many in health care. Current literature suggests there are many potential benefits that could transform future clinical workflows and decision making. Embedding AI and ML concepts in radiation therapy education could be a fundamental step in equipping radiation therapists (RTs) to engage in competent and safe practice as they utilise clinical technologies. In this discussion paper, the authors provide a brief review of some applications of AI and ML in radiation therapy and discuss pertinent considerations for radiation therapy curriculum enhancement. As the current literature suggests, AI and ML approaches will impose changes to routine clinical radiation therapy tasks. The emphasis in RT education could be on critical evaluation of AI and ML application in routine clinical workflows and gaining an understanding of the impact on quality assurance, provision of quality of care and safety in radiation therapy as well as research. It is also imperative RTs have a broader understanding of AI/ML impact on health care, including ethical and legal considerations. The paper concludes with recommendations and suggestions to deliberately embed AI and ML aspects in RT education to empower future RT practitioners.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Oncología por Radiación / Técnicos Medios en Salud / Aprendizaje Automático Tipo de estudio: Guideline / Prognostic_studies Aspecto: Ethics Límite: Humans Idioma: En Revista: J Med Imaging Radiat Sci Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Oncología por Radiación / Técnicos Medios en Salud / Aprendizaje Automático Tipo de estudio: Guideline / Prognostic_studies Aspecto: Ethics Límite: Humans Idioma: En Revista: J Med Imaging Radiat Sci Año: 2020 Tipo del documento: Article