Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice.

ABSTRACT: Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic. In this paper, we aimed to provide a series of considerations and guidelines necessary for integrating such a platform into a team's technical infrastructure and workflow. We have documented our experiences with this integration process, discussed the broader challenges encounte

SUBMITTER: Faust L 

PROVIDER: S-EPMC11245651 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature