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

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;22(11):1404 View
  4. Schönfelder A, Eberlein-Gonska M, Hülsken-Giesler M, Jovy-Klein F, Kather J, Kohoutek E, Lennefer T, Liebert E, Lipprandt M, Mathias R, Muti H, Obergassel J, Reibel T, Rösler U, Schneider M, Schlicht L, Schlieter H, Schmieding M, Schweingruber N, Sedlmayr M, Strametz R, Susec B, Wekenborg M, Weicken E, Weitz K, Diehl A, Gilbert S. Collaborative and Cooperative Hospital “In-House” Medical Device Development and Implementation in the AI Age: The European Responsible AI Development (EURAID) Framework Compatible With European Values. Journal of Medical Internet Research 2026;28:e80754 View
  5. Li G, Chen K. Optimizing edge adding through effective traversals in large scale distributed systems for reliability-related applications. Electric Power Systems Research 2026;253:112559 View
  6. Kim J, Ceballos-Arroyo A, Lin C, Liu P, Jiang H, Yadav S, Wan Q, Qin L, Young G. Automated, anatomy-based, heuristic post-processing reduces false positives and improves interpretability of deep learning intracranial aneurysm detection models. Scientific Reports 2025;16(1) View
  7. Amusa T, Okunola D, Izinyon O, Alabi A, Akinpeloye O. Strategies for Embedding Prediction Models in Clinical Decision‑Making Workflows. Cureus 2026 View
  8. Wingert T, Williams T, Syed B, Hill B, Grogan T, Young A, Antongiorgi Z, Salari V, Joosten A, Hofer I, Halperin E, Cannesson M, Gabel E. Prospective validation and real-time implementation of an automated machine learning postoperative mortality prediction model. British Journal of Anaesthesia 2026 View
  9. Cook C, Klug J, Kandler B, Baez-Suarez A, Dachowicz A, Blezek D, Missert A, Conte G, Benfield J, Mensing-Diggs A, Edwards M, Powell M, Sheedy E, Meyer H, Melnick J, Flor B, Vidal D, Sorin V, Erdal B, Langer S, Collins J, Williamson E, Korfiatis P, Kline T. State of the AI: Post-Deployment Monitoring of Radiology-Focused Internally Developed AI. Mayo Clinic Proceedings: Digital Health 2026:100342 View