Artificial intelligence in radiation oncology

Elizabeth Huynh, Ahmed Hosny, Christian Guthier, Danielle S. Bitterman, Steven E. Petit, Daphne A. Haas-Kogan, Benjamin Kann, Hugo J. W. L. Aerts*, Raymond H. Mak

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

53 Citations (Web of Science)
462 Downloads (Pure)

Abstract

The possible uses of artificial intelligence (AI) in radiation oncology are diverse and wide ranging. Herein, the authors discuss the potential applications of AI at each step of the radiation oncology workflow, which might improve the efficiency and overall quality of radiation therapy for patients with cancer. The authors also describe the associated challenges and provide their perspective on how AI platforms might change the roles of radiation oncology medical professionals.

Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex patterns in medical data and provide a quantitative, rather than purely qualitative, assessment of clinical conditions. Accordingly, AI could have particularly transformative applications in radiation oncology given the multifaceted and highly technical nature of this field of medicine with a heavy reliance on digital data processing and computer software. Indeed, AI has the potential to improve the accuracy, precision, efficiency and overall quality of radiation therapy for patients with cancer. In this Perspective, we first provide a general description of AI methods, followed by a high-level overview of the radiation therapy workflow with discussion of the implications that AI is likely to have on each step of this process. Finally, we describe the challenges associated with the clinical development and implementation of AI platforms in radiation oncology and provide our perspective on how these platforms might change the roles of radiotherapy medical professionals.

Original languageEnglish
Pages (from-to)771-781
Number of pages11
JournalNature Reviews Clinical Oncology
Volume17
Issue number12
Early online date25 Aug 2020
DOIs
Publication statusPublished - Dec 2020

Keywords

  • CELL LUNG-CANCER
  • CONVOLUTIONAL NEURAL-NETWORK
  • HEAD
  • MEDICAL PHYSICISTS
  • RADIOTHERAPY
  • RECTUM TOXICITY PREDICTION
  • SEGMENTATION
  • SPATIAL DOSE METRICS
  • SURVIVAL PREDICTION
  • THERAPY
  • SYSTEM
  • QUALITY
  • MOTION
  • ADAPTIVE NEURAL-NETWORK

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