Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”

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Autores principales:	Mirjam Pot, Barbara Prainsack
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Artificial intelligence Bias Inequity Medical physics. Medical radiology. Nuclear medicine R895-920
Acceso en línea:	https://doaj.org/article/89585920410f4c64b280fbdcbce28128
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spelling	oai:doaj.org-article:89585920410f4c64b280fbdcbce281282021-11-07T12:14:43ZReply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”10.1186/s13244-021-01088-11869-4101https://doaj.org/article/89585920410f4c64b280fbdcbce281282021-11-01T00:00:00Zhttps://doi.org/10.1186/s13244-021-01088-1https://doaj.org/toc/1869-4101Mirjam PotBarbara PrainsackSpringerOpenarticleArtificial intelligenceBiasInequityMedical physics. Medical radiology. Nuclear medicineR895-920ENInsights into Imaging, Vol 12, Iss 1, Pp 1-2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence Bias Inequity Medical physics. Medical radiology. Nuclear medicine R895-920
spellingShingle	Artificial intelligence Bias Inequity Medical physics. Medical radiology. Nuclear medicine R895-920 Mirjam Pot Barbara Prainsack Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
format	article
author	Mirjam Pot Barbara Prainsack
author_facet	Mirjam Pot Barbara Prainsack
author_sort	Mirjam Pot
title	Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_short	Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_full	Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_fullStr	Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_full_unstemmed	Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”
title_sort	reply to letter to the editor on “not all biases are bad: equitable and inequitable biases in machine learning and radiology”
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/89585920410f4c64b280fbdcbce28128
work_keys_str_mv	AT mirjampot replytolettertotheeditoronnotallbiasesarebadequitableandinequitablebiasesinmachinelearningandradiology AT barbaraprainsack replytolettertotheeditoronnotallbiasesarebadequitableandinequitablebiasesinmachinelearningandradiology
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Reply to Letter to the Editor on “Not all biases are bad: equitable and inequitable biases in machine learning and radiology”

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