Perspectives

Implicit Bias and Machine Learning in Health Care

Authors: Danish Zaidi, MD, MTS, MBE, Taylor Miller, MD, MPhil

Abstract

Machine learning (ML) has the ability to both address and entrench racial bias in healthcare delivery. Through the use of algorithms incorporating demographic data and electronic health records, we have achieved an efficient and consistent process by which to make complex (and simple) medical decisions. The onus, however, is on our medical community to ensure that such streamlining does not exacerbate the existing problem of racial bias in healthcare delivery, a concern that has come to the forefront in light of recent protests around racial justice and a pandemic that has underscored racial disparities in health outcomes.

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References

1. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402–2410.
 
2. Rajkomar A, Oren E, Chen K, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med 2018;1:18.
 
3. Goyal MK, Kuppermann N, Cleary SD, et al. Racial disparities in pain management of children with appendicitis in emergency departments. JAMA Pediatr 2015;169:996–1002.
 
4. Gershgorn D. Companies are on the hook if their hiring algorithms are biased. https://qz.com/1427621/companies-are-on-the-hook-if-their-hiring-algorithms-are-biased. Published October 22, 2018. Accessed June 29, 2020.
 
5. Lowry S, Macpherson G. A blot on the profession. Br Med J 1988;296: 657–658.
 
6. Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature 2016; 538:161–164.
 
7. Mullin E. Solving the lack of diversity in genomic research. https://www.technologyreview.com/2016/10/25/156446/solving-the-lack-of-diversity-in-genomic-research/. Published October 2016. Accessed May 17, 2018.
 
8. Pylypchuk R, Wells S, Kerr A, et al. Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study. Lancet 2018;391:1897–1907.
 
9. Dwork C, Hardt M, Pitassi T, et al. Fairness through awareness. In: ITCS ’12: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. New York: Association for Computing Machinery; 2012: 214–226.
 
10. US House of Representatives Committee on Energy and Commerce. Testimony of Mark Zuckerburg. https://docs.house.gov/meetings/IF/IF00/20180411/108090/HHRG-115-IF00-Wstate-ZuckerbergM-20180411.pdf. Published April 2018. Accessed May 18, 2018.
 
11. Beauchamp TL, Childress JF. Principles of Biomedical Ethics, 7th ed. New York: Oxford University Press; 2013.
 
12. Sharma K. Can we keep our biases from creeping into AI? https://hbr.org/2018/02/can-we-keep-our-biases-from-creeping-into-ai. Published February 2018. Accessed May 22, 2018.