MLOps in the Healthcare Domain: a Systematic Literature Review

Machine Learning Operations (MLOps) refers to the set of practices and tools designed to streamline and automate machine learning pipelines, enabling the efficient deployment and continuous evolution of ML models in production environments. In the healthcare domain, where machine learning adoption is growing, MLOps plays a crucial role in ensuring reliable, compliant, and maintainable AI systems. This systematic literature review investigates the current use of MLOps in healthcare, focusing on the practices adopted, tools used, workflow stages supported, and medical specialties involved. We conducted a structured search on scholarly databases and selected 14 primary studies published between 2015 and 2024 based on defined inclusion and exclusion criteria. Our findings reveal that while several MLOps practices and tools are being adopted in healthcare, their coverage remains uneven across the ML workflow, with early stages such as data labeling receiving little attention. Regulatory constraints further limit automation, particularly in deployment. Moreover, applications tend to concentrate on a few medical specialties, reflecting the current narrow scope of adoption. Taken together, these insights offer a structured understanding of how MLOps is currently applied in healthcare and point toward opportunities for more reliable, effective, and regulation-aware integration of machine learning in clinical contexts.