Reply to Letter to the Editor on "Not all biases are bad: equitable and inequitable biases in machine learning and radiology".
作者信息
Pot Mirjam, Prainsack Barbara
机构信息
Department of Political Science, University of Vienna, Vienna, Austria.
Department of Global Health and Social Medicine, King's College London, London, UK.
出版信息
Insights Imaging. 2021 Nov 3;12(1):157. doi: 10.1186/s13244-021-01088-1.
Abstract
摘要
相似文献
1
Reply to Letter to the Editor on "Not all biases are bad: equitable and inequitable biases in machine learning and radiology".对《致编辑的信：关于“并非所有偏差都是有害的：机器学习和放射学中的公平与不公平偏差”》的回复
Insights Imaging. 2021 Nov 3;12(1):157. doi: 10.1186/s13244-021-01088-1.
2
Letter to the editor: "Not all biases are bad: equitable and inequitable biases in machine learning and radiology".致编辑的信：“并非所有偏差都是有害的：机器学习和放射学中的公平与不公平偏差”
Insights Imaging. 2021 Jun 16;12(1):78. doi: 10.1186/s13244-021-01022-5.
3
Not all biases are bad: equitable and inequitable biases in machine learning and radiology.并非所有偏差都是有害的：机器学习与放射学中的公平偏差与不公平偏差
Insights Imaging. 2021 Feb 10;12(1):13. doi: 10.1186/s13244-020-00955-7.
4
Fairness of artificial intelligence in healthcare: review and recommendations.人工智能在医疗保健中的公平性：综述与建议。
Jpn J Radiol. 2024 Jan;42(1):3-15. doi: 10.1007/s11604-023-01474-3. Epub 2023 Aug 4.
5
The AI cycle of health inequity and digital ageism: mitigating biases through the EU regulatory framework on medical devices.健康不平等与数字年龄歧视的人工智能循环：通过欧盟医疗器械监管框架减轻偏见
J Law Biosci. 2023 Dec 7;10(2):lsad031. doi: 10.1093/jlb/lsad031. eCollection 2023 Jul-Dec.
6
Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism.人工智能、启发式偏差与健康结果优化：谨慎的乐观态度
J Clin Med. 2021 Nov 14;10(22):5284. doi: 10.3390/jcm10225284.
7
Reply to Letter to the Editor.致编辑的信的回复。
Turk J Ophthalmol. 2020 Dec 29;50(6):393. doi: 10.4274/tjo.galenos.2020.11455.
8
Reply to letter to the editor by Kharawala S, et al: Artificial intelligence for assisting systematic reviews: Opportunities with continuing challenges.对Kharawala S等人致编辑信的回复：人工智能辅助系统评价：机遇与持续挑战
J Clin Epidemiol. 2021 Oct;138:245-246. doi: 10.1016/j.jclinepi.2021.03.011. Epub 2021 Mar 19.
9
Response to Letters to the Editor regarding the editorial "Artificial intelligence, machine learning, and the human interface in medicine: is there a sweet spot for oral and maxillofacial radiology?".对致编辑的信的回复，这些信是关于题为“人工智能、机器学习与医学中的人机界面：口腔颌面放射学是否存在最佳契合点？”的社论。
Oral Surg Oral Med Oral Pathol Oral Radiol. 2019 Oct;128(4):445. doi: 10.1016/j.oooo.2019.06.007. Epub 2019 Jun 14.
10
Latent bias and the implementation of artificial intelligence in medicine.医学人工智能应用中的潜在偏见
J Am Med Inform Assoc. 2020 Dec 9;27(12):2020-2023. doi: 10.1093/jamia/ocaa094.
本文引用的文献
1
Centering Health Equity in Population Health Surveys.将健康公平置于人群健康调查的核心位置。
JAMA Health Forum. 2020 Dec 1;1(12):e201429. doi: 10.1001/jamahealthforum.2020.1429.
2
Letter to the editor: "Not all biases are bad: equitable and inequitable biases in machine learning and radiology".致编辑的信：“并非所有偏差都是有害的：机器学习和放射学中的公平与不公平偏差”
Insights Imaging. 2021 Jun 16;12(1):78. doi: 10.1186/s13244-021-01022-5.
3
Not all biases are bad: equitable and inequitable biases in machine learning and radiology.并非所有偏差都是有害的：机器学习与放射学中的公平偏差与不公平偏差
Insights Imaging. 2021 Feb 10;12(1):13. doi: 10.1186/s13244-020-00955-7.
4
Mitigating Inequities and Saving Lives with ICU Triage during the COVID-19 Pandemic.在 COVID-19 大流行期间通过 ICU 分诊减轻不平等和拯救生命。
Am J Respir Crit Care Med. 2021 Feb 1;203(3):287-295. doi: 10.1164/rccm.202010-3809CP.
5
The funhouse mirror: the I in personalised healthcare.哈哈镜：个性化医疗中的“我”。
Life Sci Soc Policy. 2021 Jan 5;17(1):1. doi: 10.1186/s40504-020-00108-0.
6
Are Clinicians Contributing to Excess African American COVID-19 Deaths? Unbeknownst to Them, They May Be.临床医生是否导致了更多非裔美国人死于新冠病毒？他们自己可能都不知道，但情况也许正是如此。
Health Equity. 2020 Apr 17;4(1):139-141. doi: 10.1089/heq.2020.0015. eCollection 2020.
7
COVID-19 exacerbating inequalities in the US.新冠疫情加剧美国的不平等现象。
Lancet. 2020 Apr 18;395(10232):1243-1244. doi: 10.1016/S0140-6736(20)30893-X.
8
Combating COVID-19: health equity matters.抗击新冠疫情：健康公平至关重要。
Nat Med. 2020 Apr;26(4):458. doi: 10.1038/s41591-020-0823-6.
9
The Impact of Unconscious Bias in Healthcare: How to Recognize and Mitigate It.无意识偏见在医疗保健中的影响：如何识别和减轻它。
J Infect Dis. 2019 Aug 20;220(220 Suppl 2):S62-S73. doi: 10.1093/infdis/jiz214.
10
Methodological Considerations for Advancing Research on the Health and Wellbeing of Sexual and Gender Minority Youth.推进性少数和性别少数青年健康和福祉研究的方法学考虑因素。
LGBT Health. 2019 May-Jun;6(4):156-165. doi: 10.1089/lgbt.2018.0141.
Zotero 插件