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 <Emphasis Type="BoldItalic">Question</Emphasis> MedML applications are becoming increasingly adopted in clinics, but the necessary infrastructure to sustain these applications is currently not well-defined. <Emphasis Type="BoldItalic">Findings</Emphasis> Adapting machine learning operations concepts enhances MedML ecosystems by improving interoperability, automating monitoring/validation, and reducing deployment burdens on clinicians and medical informaticians. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> 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.

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

期刊论文
出版模式：

开放获取
发表日期：

2025年5月8日

查看全文

José Guilherme de Almeida,
Christina Messiou,
Sam J. Withey,
Celso Matos,
Dow-Mu Koh,
Nickolas Papanikolaou

摘要

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.