Biases in Machine Learning in Healthcare

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

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-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 languageEnglish
Title of host publicationAI in Clinical Medicine
Subtitle of host publicationA Practical Guide for Healthcare Professionals
EditorsMichael F. Byrne, Nasim Parsa, Alexandra T. Greenhill, Daljeet Chahal, Omer Ahmad, Ulas Bagci
Place of PublicationUnited States
PublisherWiley-Blackwell
Chapter39
Pages426-436
Number of pages11
Edition1st
ISBN (Electronic)9781119790679
ISBN (Print)9781119790648
DOIs
Publication statusPublished - 2023

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