As its applications become increasingly widespread, machine learning (ML) is reshaping global labour landscapes, including in the medical sector. When it comes to medical work, ML is often mobilised to intervene in the workforce shortages that engender the future sustainability of healthcare systems. This chapter addresses claims depicting ML technologies as labour-saving by dwelling on the reconfiguration they attempt to generate within medical work. Specifically, it proposes examining medical work in the wake of ML by zooming in on how ML logics attempt to reconceptualise and intervene upon medical work. It does so, first, by reviewing recent literature on ML applications in clinical practice and their implications for medical work. Subsequently, it provides an empirical analysis of two case studies of ML as it is developed for and tested in acute care. It proposes a conceptualisation of ML as a labour-redirecting technology, which achieves efficiency by leaving some tasks at least temporarily unperformed. Given that tasks that are not organisationally visible and machine-readable fall outside the scope of this technology, this chapter proposes that ML is likely to result in an intensification of professionals’ labour in organisational settings.