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Scaling equitable artificial intelligence in healthcare with machine learning operations
5
Zitationen
5
Autoren
2024
Jahr
Abstract
Machine learning operations (MLOps), a discipline concerned with the production, monitoring and maintenance of artificial intelligence (AI) and machine learning (ML) models at scale, applied in healthcare can facilitate the transition of AI/ML-enabled healthcare tools from research to sustainable deployment.1–3 Adherence to MLOps best practices can address persistent challenges with AI/ML tools deployed into clinical workflows where models often struggle with generalisability, integration and robustness. As AI regulations continue to evolve such as the Department of Health and Human Services Office of Civil Rights final ruling that requires healthcare providers to ensure their AI/ML tools do not discriminate,4 it becomes increasingly essential for MLOps in healthcare to prioritise health equity.
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