This paper is in the following e-collection/theme issue:

Viewpoints on and Experiences with Digital Technologies in Health (96) Registered Report (770) Data Science (354) Machine Learning (2029) Methods and Instruments in Medical Informatics (185) Quality Improvement (290) Design of Processes and Workflows (181) Safety and Error Prevention in Health (238)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50437, first published .
Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice

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

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

Authors of this article:

Louis Faust1 Author Orcid Image ;   Patrick Wilson1 Author Orcid Image ;   Shusaku Asai1 Author Orcid Image ;   Sunyang Fu2 Author Orcid Image ;   Hongfang Liu2 Author Orcid Image ;   Xiaoyang Ruan2 Author Orcid Image ;   Curt Storlie1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Feng J, Xia F, Singh K, Pirracchio R. Not All Clinical AI Monitoring Systems Are Created Equal: Review and Recommendations. NEJM AI 2025;2(2) View
  2. Ma C, Qiu L. Unveiling the power of R: a comprehensive perspective for laboratory medicine data analysis. Clinical Chemistry and Laboratory Medicine (CCLM) 2025;63(8):1458 View
  3. Sorin V, Korfiatis P, Bratt A, Leiner T, Wald C, Butler C, Cook C, Kline T, Collins J. Using a Large Language Model for Postdeployment Monitoring of FDA-Approved Artificial Intelligence: Pulmonary Embolism Detection Use Case. Journal of the American College of Radiology 2025 View