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Cited by 23Artificial Intelligence in Healthcare and EducationRadiology practices and educationRadiomics and Machine Learning in Medical Imaging

Medical machine learning operations: A framework to facilitate clinical AI development and deployment in radiology

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de Almeida, José Guilherme, Messiou, Christina, Withey, Sam J., Matos, Celso, Koh, Dow-Mu, Papanikolaou, Nickolas·2025·European Radiology
DOI 10.1007/s00330-025-11654-6PMC PMC12559124
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Abstract

The integration of machine-learning technologies into radiology practice has the potential to significantly enhance diagnostic workflows and patient care. However, the successful deployment and maintenance of medical machine-learning (MedML) systems in radiology requires robust operational frameworks. Medical machine-learning operations (MedMLOps) offer a structured approach ensuring persistent MedML reliability, safety, and clinical relevance. MedML systems are increasingly employed to analyse sensitive clinical and radiological data, which continuously changes due to advancements in data acquisition and model development. These systems can alleviate the workload of radiologists by streamlining diagnostic tasks, such as image interpretation and triage. MedMLOps ensures that such systems stay accurate and dependable by facilitating continuous performance monitoring, systematic validation, and simplified model maintenance—all critical to maintaining trust in machine-learning-driven diagnostics. Furthermore, MedMLOps aligns with established principles of patient data protection and regulatory compliance, including recent developments in the European Union, emphasising transparency, documentation, and safe model retraining. This enables radiologists to implement modern machine-learning tools with control and oversight at the forefront, ensuring reliable model performance within the dynamic context of clinical practice. MedMLOps empowers radiologists to deliver consistent, high-quality care with confidence, ensuring that MedML systems stay aligned with evolving medical standards and patient needs. MedMLOps can assist multiple stakeholders in radiology by ensuring models are available, continuously monitored and easy to use and maintain while preserving patient privacy. MedMLOps can better serve patients by facilitating the clinical implementation of cutting-edge MedML and clinicians by ensuring that MedML models are only utilised when they are performing as expected. Key Points: Question MedML applications are becoming increasingly adopted in clinics, but the necessary infrastructure to sustain these applications is currently not well-defined . Findings Adapting machine learning operations concepts enhances MedML ecosystems by improving interoperability, automating monitoring/validation, and reducing deployment burdens on clinicians and medical informaticians . Clinical relevance Implementing these solutions eases the faster and safer adoption of advanced MedML models, ensuring consistent performance while reducing workload for clinicians, benefiting patient care through streamlined diagnostic workflows .

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Authors
de Almeida, J. G., Messiou, C., Withey, S. J., Matos, C., Koh, D.-M., & Papanikolaou, N.
Affiliations
  • Royal Marsden Hospital (GB)
  • Champalimaud Foundation (PT)
ORCID
Venue
European Radiology
Publisher
Springer Science and Business Media LLC
Published
2025-05-08
Volume
35
Issue
11
Pages
6828-6841
ISSN
  • 0938-7994 (ISSN-L)
  • 1432-1084
DOI
10.1007/s00330-025-11654-6
PMC
PMC12559124
OpenAlex
W4410193416
Cited by
23
References
123
Citations by year
2026: 17 · 2025: 6
FWCI
1.36
Type
journal article
Language
en
Open access
Licence
Locations
  • European Radiology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Horizon 2020 (952159)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
machine learning in medicine, MLOps, radiology, clinical implementation of AI, model monitoring, model validation, model maintenance, model retraining, patient data protection, regulatory compliance, clinical implementation, diagnostic workflow, radiologist workload
MeSH terms
Machine Learning, Artificial Intelligence, Humans, Radiology, Workflow

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