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Artificial intelligence in radiation oncology treatment planning: a brief overview

	author = {Kendall J. Kiser and Clifton D. Fuller and Valerie K. Reed},
	title = {Artificial intelligence in radiation oncology treatment planning: a brief overview},
	journal = {Journal of Medical Artificial Intelligence},
	volume = {2},
	number = {0},
	year = {2019},
	keywords = {},
	abstract = {Among medical specialties, radiation oncology has long been an innovator and early adopter of therapeutic technologies. This specialty is now situated in prime position to be revolutionized by advances in artificial intelligence (AI), especially machine and deep learning. AI has been investigated by radiation oncologists and physicists in both general and niche radiotherapy planning tasks and has often demonstrated performance that is indistinguishable from human experts, while substantially shortening the time required to complete these tasks. We sought to review applications of AI to domains germane to radiation oncology, namely: image segmentation, treatment plan generation and optimization, normal tissue complication probability modeling, quality assurance (QA), and adaptive re-planning. We sought likewise to consider obstacles to AI adoption in the radiotherapy clinic, now primarily political, legal, and ethical rather than technical in nature.},
	issn = {2617-2496},	url = {}