Considerations for addressing bias in artificial intelligence for health equity

Michael D Abràmoff; Michelle E Tarver; Nilsa Loyo-Berrios; Sylvia Trujillo; Danton Char; Ziad Obermeyer; Malvina B Eydelman; Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group of the Collaborative Community for Ophthalmic Imaging Foundation, Washington, D.C; William H Maisel

doi:10.1038/s41746-023-00913-9

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Considerations for addressing bias in artificial intelligence for health equity

Journal article

Open access

Peer reviewed

Considerations for addressing bias in artificial intelligence for health equity

Michael D Abràmoff, Michelle E Tarver, Nilsa Loyo-Berrios, Sylvia Trujillo, Danton Char, Ziad Obermeyer, Malvina B Eydelman, Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group of the Collaborative Community for Ophthalmic Imaging Foundation, Washington, D.C and William H Maisel

NPJ digital medicine, Vol.6(1), 170

09/12/2023

DOI: 10.1038/s41746-023-00913-9

PMCID: PMC10497548

PMID: 37700029

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Abstract

Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these “Considerations” is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all.

Artificial intelligence

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Title: Subtitle: Considerations for addressing bias in artificial intelligence for health equity
Creators: Michael D Abràmoff - University of Iowa
Michelle E Tarver - Center for Devices and Radiological Health
Nilsa Loyo-Berrios - Center for Devices and Radiological Health
Sylvia Trujillo - Ochin
Danton Char - Ethics and Public Policy Center
Ziad Obermeyer - University of California, Berkeley
Malvina B Eydelman - Center for Devices and Radiological Health
Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group of the Collaborative Community for Ophthalmic Imaging Foundation, Washington, D.C
William H Maisel - Center for Devices and Radiological Health
Resource Type: Journal article
Publication Details: NPJ digital medicine, Vol.6(1), 170
DOI: 10.1038/s41746-023-00913-9
PMID: 37700029
PMCID: PMC10497548
NLM abbreviation: NPJ Digit Med
ISSN: 2398-6352
eISSN: 2398-6352
Publisher: Nature Publishing Group
Language: English
Date published: 09/12/2023
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Fraternal Order of Eagles Diabetes Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984465559202771

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