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On Algorithmic Fairness in Medical Practice
by
Keeling, Geoff
, Grote, Thomas
in
Accuracy
/ Algorithms
/ Artificial intelligence
/ Bias
/ Bioethics and Information Technology
/ Data Collection
/ Delivery of Health Care
/ Departments and Columns
/ Diagnostic tests
/ Fairness
/ Health care
/ Health disparities
/ Health risk assessment
/ Health risks
/ Health services
/ Heart attacks
/ Hospitals
/ Humans
/ Inequality
/ Intensive care
/ Machine Learning
/ Medical conditions
/ Medical diagnosis
/ Medical ethics
/ Medical personnel
/ Medical practices
/ Medical treatment
/ Medicine
/ Patients
/ Social Justice
2022
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On Algorithmic Fairness in Medical Practice
by
Keeling, Geoff
, Grote, Thomas
in
Accuracy
/ Algorithms
/ Artificial intelligence
/ Bias
/ Bioethics and Information Technology
/ Data Collection
/ Delivery of Health Care
/ Departments and Columns
/ Diagnostic tests
/ Fairness
/ Health care
/ Health disparities
/ Health risk assessment
/ Health risks
/ Health services
/ Heart attacks
/ Hospitals
/ Humans
/ Inequality
/ Intensive care
/ Machine Learning
/ Medical conditions
/ Medical diagnosis
/ Medical ethics
/ Medical personnel
/ Medical practices
/ Medical treatment
/ Medicine
/ Patients
/ Social Justice
2022
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Do you wish to request the book?
On Algorithmic Fairness in Medical Practice
by
Keeling, Geoff
, Grote, Thomas
in
Accuracy
/ Algorithms
/ Artificial intelligence
/ Bias
/ Bioethics and Information Technology
/ Data Collection
/ Delivery of Health Care
/ Departments and Columns
/ Diagnostic tests
/ Fairness
/ Health care
/ Health disparities
/ Health risk assessment
/ Health risks
/ Health services
/ Heart attacks
/ Hospitals
/ Humans
/ Inequality
/ Intensive care
/ Machine Learning
/ Medical conditions
/ Medical diagnosis
/ Medical ethics
/ Medical personnel
/ Medical practices
/ Medical treatment
/ Medicine
/ Patients
/ Social Justice
2022
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Journal Article
On Algorithmic Fairness in Medical Practice
2022
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Overview
The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine.
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