mlsanityData to 7 Oct 2026

MLHOps: Machine Learning for Healthcare Operations

arXiv 2305.02474 ↗First posted 4 May 2023cs.LGcited by 2

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Abstract (arXiv)

Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. This paper provides both a survey of work in this area and guidelines for developers and clinicians to deploy and maintain their own models in clinical practice. We cover the foundational concepts of general machine learning operations, describe the initial setup of MLHOps pipelines (including data sources, preparation, engineering, and tools). We then describe long-term monitoring and updating (including data distribution shifts and model updating) and ethical considerations (including bias, fairness, interpretability, and privacy). This work therefore provides guidance across the full pipeline of MLHOps from conception to initial and ongoing deployment.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 131 of its 301 references that are in the map. First posted on arXiv 4 May 2023.

Builds on

131 of 301 references.

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38 more
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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Healthcited by 8
  2. 2025Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradationcited by 6