Operationalising fairness in medical algorithms

doi:10.1136/bmjhci-2022-100617

Editorial

. 2022 Jun;29(1):e100617.

doi: 10.1136/bmjhci-2022-100617.

Operationalising fairness in medical algorithms

Sonali Parbhoo¹, Judy Wawira Gichoya², Leo Anthony Celi^{3

4}, Miguel Ángel Armengol de la Hoz⁵; for MIT Critical Data

Affiliations

¹ Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.
² Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA.
³ Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USA LCeli@mit.edu.
⁴ Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
⁵ Big Data Department, Regional Ministry of Health of Southern Spain, Sevilla, Spain.

PMID: 35688512
PMCID: PMC9189822
DOI: 10.1136/bmjhci-2022-100617

Editorial

Operationalising fairness in medical algorithms

Sonali Parbhoo et al. BMJ Health Care Inform. 2022 Jun.

. 2022 Jun;29(1):e100617.

doi: 10.1136/bmjhci-2022-100617.

Authors

Sonali Parbhoo¹, Judy Wawira Gichoya², Leo Anthony Celi^{3

4}, Miguel Ángel Armengol de la Hoz⁵; for MIT Critical Data

Affiliations

¹ Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.
² Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA.
³ Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USA LCeli@mit.edu.
⁴ Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
⁵ Big Data Department, Regional Ministry of Health of Southern Spain, Sevilla, Spain.

PMID: 35688512
PMCID: PMC9189822
DOI: 10.1136/bmjhci-2022-100617

No abstract available

Keywords: Artificial intelligence; Machine Learning.

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Conflict of interest statement

Competing interests: None declared.

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References

1. Wawira Gichoya J, McCoy LG, Celi LA, et al. . Equity in essence: a call for operationalising fairness in machine learning for healthcare. BMJ Health Care Inform 2021;28:e100289. 10.1136/bmjhci-2020-100289 - DOI - PMC - PubMed
1. McCoy LG, Banja JD, Ghassemi M, et al. . Ensuring machine learning for healthcare works for all. BMJ Health Care Inform 2020;27:e100237. 10.1136/bmjhci-2020-100237 - DOI - PMC - PubMed
1. Sarkar R, Martin C, Mattie H, et al. . Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study. Lancet Digit Health 2021;3:e241–9. 10.1016/S2589-7500(21)00022-4 - DOI - PMC - PubMed
1. Zink A, Rose S. Identifying undercompensated groups defined by multiple attributes in risk adjustment. BMJ Health Care Inform 2021;28:e100414. 10.1136/bmjhci-2021-100414 - DOI - PMC - PubMed
1. Cerrato P, Halamka J, Pencina M. A proposal for developing a platform that evaluates algorithmic equity and accuracy. BMJ Health Care Inform 2022;29:e100423. 10.1136/bmjhci-2021-100423 - DOI - PMC - PubMed

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[1] Wawira Gichoya J, McCoy LG, Celi LA, et al. . Equity in essence: a call for operationalising fairness in machine learning for healthcare. BMJ Health Care Inform 2021;28:e100289. 10.1136/bmjhci-2020-100289 - DOI - PMC - PubMed

[2] Wawira Gichoya J, McCoy LG, Celi LA, et al. . Equity in essence: a call for operationalising fairness in machine learning for healthcare. BMJ Health Care Inform 2021;28:e100289. 10.1136/bmjhci-2020-100289 - DOI - PMC - PubMed

[3] McCoy LG, Banja JD, Ghassemi M, et al. . Ensuring machine learning for healthcare works for all. BMJ Health Care Inform 2020;27:e100237. 10.1136/bmjhci-2020-100237 - DOI - PMC - PubMed

[4] McCoy LG, Banja JD, Ghassemi M, et al. . Ensuring machine learning for healthcare works for all. BMJ Health Care Inform 2020;27:e100237. 10.1136/bmjhci-2020-100237 - DOI - PMC - PubMed

[5] Sarkar R, Martin C, Mattie H, et al. . Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study. Lancet Digit Health 2021;3:e241–9. 10.1016/S2589-7500(21)00022-4 - DOI - PMC - PubMed

[6] Sarkar R, Martin C, Mattie H, et al. . Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study. Lancet Digit Health 2021;3:e241–9. 10.1016/S2589-7500(21)00022-4 - DOI - PMC - PubMed

[7] Zink A, Rose S. Identifying undercompensated groups defined by multiple attributes in risk adjustment. BMJ Health Care Inform 2021;28:e100414. 10.1136/bmjhci-2021-100414 - DOI - PMC - PubMed

[8] Zink A, Rose S. Identifying undercompensated groups defined by multiple attributes in risk adjustment. BMJ Health Care Inform 2021;28:e100414. 10.1136/bmjhci-2021-100414 - DOI - PMC - PubMed

[9] Cerrato P, Halamka J, Pencina M. A proposal for developing a platform that evaluates algorithmic equity and accuracy. BMJ Health Care Inform 2022;29:e100423. 10.1136/bmjhci-2021-100423 - DOI - PMC - PubMed

[10] Cerrato P, Halamka J, Pencina M. A proposal for developing a platform that evaluates algorithmic equity and accuracy. BMJ Health Care Inform 2022;29:e100423. 10.1136/bmjhci-2021-100423 - DOI - PMC - PubMed

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Operationalising fairness in medical algorithms

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Operationalising fairness in medical algorithms

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