Literature DB >> 31668208

Artificial Intelligence in Radiation Oncology.

Christopher R Deig1, Aasheesh Kanwar1, Reid F Thompson2.   

Abstract

The integration of artificial intelligence in the radiation oncologist's workflow has multiple applications and significant potential. From the initial patient encounter, artificial intelligence may aid in pretreatment disease outcome and toxicity prediction. It may subsequently aid in treatment planning, and enhanced dose optimization. Artificial intelligence may also optimize the quality assurance process and support a higher level of safety, quality, and efficiency of care. This article describes components of the radiation consultation, planning, and treatment process and how the thoughtful integration of artificial intelligence may improve shared decision making, planning efficiency, planning quality, patient safety, and patient outcomes. Published by Elsevier Inc.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Machine learning

Mesh:

Year:  2019        PMID: 31668208     DOI: 10.1016/j.hoc.2019.08.003

Source DB:  PubMed          Journal:  Hematol Oncol Clin North Am        ISSN: 0889-8588            Impact factor:   3.722


  2 in total

1.  Using national data to model the New Zealand radiation oncology workforce.

Authors:  Alex Dunn; Shaun Costello; Fiona Imlach; Emmanuel Jo; Jason Gurney; Rose Simpson; Diana Sarfati
Journal:  J Med Imaging Radiat Oncol       Date:  2022-06-29       Impact factor: 1.667

Review 2.  Shared Decision-Making and Medicolegal Aspects: Delivering High-Quality Cancer Care in India.

Authors:  Dinesh C Doval; Prabhash Kumar; Vineet Talwar; Ashok K Vaid; Chirag Desai; Vikas Ostwal; Palanki S Dattatreya; Vijay Agarwal; Vaibhav Saxena
Journal:  Indian J Palliat Care       Date:  2020-11-19
  2 in total

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