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Ciclos de exclusión: reforzar las disparidades en la medicina
Ana Bracic1, Shawneequa L Callier2,3, W Nicholson Price4,5
1Department of Political Science, Michigan State University, East Lansing, MI, USA.
Science (New York, N.Y.)
|September 8, 2022
Resumen
El desarrollo de la inteligencia artificial médica (IA) se ve obstaculizado por los ciclos de exclusión. Estos ciclos implican la práctica clínica y la recopilación de datos, creando ciclos de retroalimentación que se refuerzan y perpetúan la inequidad.
Área de la Ciencia:
- La inteligencia artificial médica
- Equidad en salud
- Informática clínica
Sus antecedentes:
- La práctica clínica y los métodos de recopilación de datos pueden incorporar sesgos sistémicos.
- Los modelos de inteligencia artificial (IA) médica existentes pueden reflejar y amplificar estos sesgos.
Objetivo del estudio:
- Analizar la interconexión de la práctica clínica, la recopilación de datos y la IA médica.
- Identificar cómo estos elementos crean ciclos de exclusión que se refuerzan por sí mismos.
Principales métodos:
- Análisis conceptual de la literatura existente sobre la IA en el cuidado de la salud.
- Examen de los circuitos de retroalimentación entre los flujos de trabajo clínicos, la procedencia de los datos y el desarrollo algorítmico.
Principales resultados:
- La práctica clínica, la recopilación de datos y la IA médica forman ciclos de exclusión que interactúan.
- Estos ciclos perpetúan las desigualdades al limitar la diversidad y la representatividad de los datos utilizados para la capacitación y validación de la IA.
Conclusiones:
- Abordar la exclusión en la IA médica requiere un enfoque sistémico.
- Las intervenciones deben centrarse en la práctica clínica y la recopilación de datos para garantizar un desarrollo y despliegue equitativos de la IA.
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