Predictive model transferability is gaining more attention as healthcare organizations attempt to implement artificial intelligence (AI)-based prediction tools. Although some machine learning (ML)-based models fail when subjected to retrospective validation across institutions and patient populations, technical improvements show promise for addressing this model efficacy problem. To address the engineering challenges, a technical subfield labelled MLOps has emerged.
However, the focus of MLOps on technical transferability may be obscuring a larger set of obstacles to sociotechnical transferability: organizational, social, and individual challenges of deploying models at scale across contexts, whether institutions, teams or individual roles.
From Nature Machine Intelligence
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