Participant flow diagrams for health equity in AI.

Autor: Ellen JG; Harvard Medical School, Boston, MA, USA. Electronic address: jellen@hms.harvard.edu., Matos J; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Faculty of Engineering, University of Porto, Porto, Portugal; Institute for Systems and Computer Engineering, Technology and Science (INESCTEC), Porto, Portugal., Viola M; Harvard Medical School, Boston, MA, USA., Gallifant J; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Critical Care, Guy's and St Thomas' NHS Trust, London, United Kingdom., Quion J; University of the East Ramon Magsaysay Memorial Medical School, Quezon City, Philippines., Anthony Celi L; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA., Abu Hussein NS; Pulmonary, Critical Care & Sleep Medicine, Yale School of Medicine, CT, USA.
Jazyk: angličtina
Zdroj: Journal of biomedical informatics [J Biomed Inform] 2024 Apr; Vol. 152, pp. 104631. Date of Electronic Publication: 2024 Mar 27.
DOI: 10.1016/j.jbi.2024.104631
Abstrakt: Selection bias can arise through many aspects of a study, including recruitment, inclusion/exclusion criteria, input-level exclusion and outcome-level exclusion, and often reflects the underrepresentation of populations historically disadvantaged in medical research. The effects of selection bias can be further amplified when non-representative samples are used in artificial intelligence (AI) and machine learning (ML) applications to construct clinical algorithms. Building on the "Data Cards" initiative for transparency in AI research, we advocate for the addition of a participant flow diagram for AI studies detailing relevant sociodemographic and/or clinical characteristics of excluded participants across study phases, with the goal of identifying potential algorithmic biases before their clinical implementation. We include both a model for this flow diagram as well as a brief case study explaining how it could be implemented in practice. Through standardized reporting of participant flow diagrams, we aim to better identify potential inequities embedded in AI applications, facilitating more reliable and equitable clinical algorithms.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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