Biases in Machine Learning in Healthcare

Dora C. Huang; Leo A. Celi; Zach O'Brien

doi:10.1002/9781119790686.ch39

Biases in Machine Learning in Healthcare

Dora C. Huang, Leo A. Celi, Zach O'Brien

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Other › peer-review

Abstract

Machine learning in healthcare (MLHC) has the potential to revolutionize healthcare and health systems research. However, these benefits must be weighed against the risks of MLHC in perpetuating or even magnifying existing health disparities. This chapter discusses existing and historical biases in clinical medicine, examines the potential hazards associated with MLHC implementation, and considers possible solutions to mitigate these concerns.

Original language	English
Title of host publication	AI in Clinical Medicine
Subtitle of host publication	A Practical Guide for Healthcare Professionals
Editors	Michael F. Byrne, Nasim Parsa, Alexandra T. Greenhill, Daljeet Chahal, Omer Ahmad, Ulas Bagci
Place of Publication	United States
Publisher	Wiley-Blackwell
Chapter	39
Pages	426-436
Number of pages	11
Edition	1st
ISBN (Electronic)	9781119790679
ISBN (Print)	9781119790648
DOIs	https://doi.org/10.1002/9781119790686.ch39
Publication status	Published - 2023

Access to Document

10.1002/9781119790686.ch39

Cite this

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title = "Biases in Machine Learning in Healthcare",

abstract = "Machine learning in healthcare (MLHC) has the potential to revolutionize healthcare and health systems research. However, these benefits must be weighed against the risks of MLHC in perpetuating or even magnifying existing health disparities. This chapter discusses existing and historical biases in clinical medicine, examines the potential hazards associated with MLHC implementation, and considers possible solutions to mitigate these concerns.",

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year = "2023",

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publisher = "Wiley-Blackwell",

address = "Australia",

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Biases in Machine Learning in Healthcare. / Huang, Dora C.; Celi, Leo A.; O'Brien, Zach.
AI in Clinical Medicine: A Practical Guide for Healthcare Professionals. ed. / Michael F. Byrne; Nasim Parsa; Alexandra T. Greenhill; Daljeet Chahal; Omer Ahmad; Ulas Bagci. 1st. ed. United States: Wiley-Blackwell, 2023. p. 426-436.

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Other › peer-review

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T1 - Biases in Machine Learning in Healthcare

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AU - Celi, Leo A.

AU - O'Brien, Zach

PY - 2023

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N2 - Machine learning in healthcare (MLHC) has the potential to revolutionize healthcare and health systems research. However, these benefits must be weighed against the risks of MLHC in perpetuating or even magnifying existing health disparities. This chapter discusses existing and historical biases in clinical medicine, examines the potential hazards associated with MLHC implementation, and considers possible solutions to mitigate these concerns.

AB - Machine learning in healthcare (MLHC) has the potential to revolutionize healthcare and health systems research. However, these benefits must be weighed against the risks of MLHC in perpetuating or even magnifying existing health disparities. This chapter discusses existing and historical biases in clinical medicine, examines the potential hazards associated with MLHC implementation, and considers possible solutions to mitigate these concerns.

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DO - 10.1002/9781119790686.ch39

M3 - Chapter (Book)

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BT - AI in Clinical Medicine

A2 - Byrne, Michael F.

A2 - Parsa, Nasim

A2 - Greenhill, Alexandra T.

A2 - Chahal, Daljeet

A2 - Ahmad, Omer

A2 - Bagci, Ulas

PB - Wiley-Blackwell

CY - United States

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