TY - JOUR
T1 - Artificial intelligence in radiation oncology
AU - Huynh, Elizabeth
AU - Hosny, Ahmed
AU - Guthier, Christian
AU - Bitterman, Danielle S.
AU - Petit, Steven E.
AU - Haas-Kogan, Daphne A.
AU - Kann, Benjamin
AU - Aerts, Hugo J. W. L.
AU - Mak, Raymond H.
N1 - Funding Information:
The authors acknowledge financial support from the US NIH (grants U24CA194354, U01CA190234, U01CA209414 and R35CA220523).
Publisher Copyright:
© 2020, Springer Nature Limited.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - CELL LUNG-CANCER
KW - CONVOLUTIONAL NEURAL-NETWORK
KW - HEAD
KW - MEDICAL PHYSICISTS
KW - RADIOTHERAPY
KW - RECTUM TOXICITY PREDICTION
KW - SEGMENTATION
KW - SPATIAL DOSE METRICS
KW - SURVIVAL PREDICTION
KW - THERAPY
KW - SYSTEM
KW - QUALITY
KW - MOTION
KW - ADAPTIVE NEURAL-NETWORK
U2 - 10.1038/s41571-020-0417-8
DO - 10.1038/s41571-020-0417-8
M3 - (Systematic) Review article
C2 - 32843739
SN - 1759-4774
VL - 17
SP - 771
EP - 781
JO - Nature Reviews Clinical Oncology
JF - Nature Reviews Clinical Oncology
IS - 12
ER -