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

Area of Science:

• Radiology and Medical Imaging
• Artificial Intelligence in Healthcare
• Machine Learning Operations

Background:

• Machine learning (ML) integration in radiology enhances diagnostics and patient care.
• Deployment and maintenance of medical ML (MedML) systems require robust operational frameworks.
• MedML systems analyze sensitive clinical data, necessitating continuous updates and validation.

Purpose of the Study:

• To introduce Medical Machine Learning Operations (MedMLOps) as a structured approach for MedML systems in radiology.
• To ensure persistent reliability, safety, and clinical relevance of MedML applications.
• To address the need for robust infrastructure to sustain increasing MedML adoption in clinics.

Main Methods:

• Adapting machine learning operations (MLOps) principles to the medical domain (MedMLOps).
• Implementing continuous performance monitoring and systematic validation of MedML models.
• Ensuring transparency, documentation, and safe model retraining for regulatory compliance.

Main Results:

• MedMLOps enhances MedML ecosystems by improving interoperability and automating monitoring/validation.
• Deployment burdens on clinicians and medical informaticians are reduced.
• Reliable model performance is maintained within the dynamic clinical practice context.

Conclusions:

• MedMLOps facilitates faster and safer adoption of advanced MedML models in radiology.
• Consistent performance of MedML models is ensured, reducing clinician workload.
• Patient care is improved through streamlined diagnostic workflows and trustworthy AI tools.