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Operationalising fairness in medical algorithms.

作者信息

Parbhoo Sonali, Wawira Gichoya Judy, Celi Leo Anthony, de la Hoz Miguel Ángel Armengol

机构信息

Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.

Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA.

出版信息

BMJ Health Care Inform. 2022 Jun;29(1). doi: 10.1136/bmjhci-2022-100617.

Abstract
摘要

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本文引用的文献

1
Operationalising fairness in medical AI adoption: detection of early Alzheimer's disease with 2D CNN.
BMJ Health Care Inform. 2022 Apr;29(1). doi: 10.1136/bmjhci-2021-100485.
3
A proposal for developing a platform that evaluates algorithmic equity and accuracy.
BMJ Health Care Inform. 2022 Apr;29(1). doi: 10.1136/bmjhci-2021-100423.
4
Global disparity bias in ophthalmology artificial intelligence applications.
BMJ Health Care Inform. 2022 Apr;29(1). doi: 10.1136/bmjhci-2021-100470.
6
Resampling to address inequities in predictive modeling of suicide deaths.
BMJ Health Care Inform. 2022 Apr;29(1). doi: 10.1136/bmjhci-2021-100456.
7
Can medical algorithms be fair? Three ethical quandaries and one dilemma.
BMJ Health Care Inform. 2022 Apr;29(1). doi: 10.1136/bmjhci-2021-100445.
8
Identifying undercompensated groups defined by multiple attributes in risk adjustment.
BMJ Health Care Inform. 2021 Sep;28(1). doi: 10.1136/bmjhci-2021-100414.
9
Equity in essence: a call for operationalising fairness in machine learning for healthcare.
BMJ Health Care Inform. 2021 Apr;28(1). doi: 10.1136/bmjhci-2020-100289.

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