Participant flow diagrams for health equity in AI.
J Biomed Inform
; 152: 104631, 2024 04.
Article
en En
| MEDLINE
| ID: mdl-38548006
ABSTRACT
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.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Equidad en Salud
/
Investigación Biomédica
Límite:
Humans
Idioma:
En
Revista:
J Biomed Inform
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article