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Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review.

Yutong Li1, Julie Tian1, Ariana Xu1

  • 1Department of Psychiatry, University of Alberta, 4-142 KATZ, Edmonton, AB, T6G 2R3, Canada, 1 780-407-6504.

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Summary
This summary is machine-generated.

This study explores Machine Learning Operations (MLOps) in healthcare, proposing a maturity framework. Findings highlight the need for better infrastructure and stakeholder engagement to advance ML applications in clinical practice.

Keywords:
MLMLOpsclinical practicedatadatabasehealth carehealth care applicationshealth care implementationsmachine learningmachine learning operationsmaturity frameworkscoping review

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Area of Science:

  • Healthcare Informatics
  • Machine Learning Engineering
  • Clinical AI Implementation

Background:

  • The application of machine learning (ML) in medicine is rapidly expanding, yet practical implementation in clinical settings faces significant barriers.
  • Machine Learning Operations (MLOps) practices, common in IT, are under-researched in healthcare, limiting the deployment of ML models.
  • Existing literature lacks comprehensive details on the feasibility and operationalization of MLOps within the unique context of healthcare.

Purpose of the Study:

  • To investigate and detail the implementation of MLOps within healthcare environments.
  • To propose a novel MLOps maturity framework tailored for healthcare applications.
  • To identify key components and considerations for successful MLOps deployment in the medical field.

Main Methods:

  • A scoping review was performed following the Joanna Briggs Institute Manual for Evidence Synthesis.
  • Four major databases (MEDLINE, Embase, Web of Science, Scopus) were searched for studies on MLOps proof-of-concept or real-world implementation in healthcare.
  • A 3-stage basic qualitative content analysis was used to synthesize findings from 19 included studies.

Main Results:

  • The MLOps workflow in healthcare encompasses data extraction, preparation, model training, evaluation, validation, deployment, continuous monitoring, and continual learning.
  • A 3-stage MLOps maturity framework (low, partial, full) was proposed, with 13 of 19 studies demonstrating full maturity.
  • Eight studies addressed critical ethical, legislative, and stakeholder considerations for MLOps in healthcare.

Conclusions:

  • Limited studies report on ML implementation in healthcare, underscoring the need for improved data infrastructure and collaborative development.
  • Engaging patients, policymakers, and healthcare professionals is crucial for the successful creation and implementation of ML healthcare applications.
  • Variations in study quality impacted the depth of analysis for each MLOps workflow step, highlighting a limitation in current research.