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Artificial intelligence algorithm optimization and application in patient-based real-time quality control (PBRTQC).

Bowen Su1, Yanpeng Zhang1, Xiaomin Shi2

  • 1Department of Laboratory Medicine, Peking University First Hospital, Beijing 100034, China; School of Basic Medical Sciences, Peking University, Beijing 100191, China.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|March 7, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances patient-based real-time quality control (PBRTQC) in clinical labs. This review details AI-driven PBRTQC applications, challenges, and future trends for intelligent laboratory monitoring.

Keywords:
Artificial intelligenceDeep learningLaboratory managementMachine learningPBRTQCQuality control

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Area of Science:

  • Clinical Laboratory Science
  • Artificial Intelligence in Healthcare
  • Quality Management Systems

Background:

  • Patient-based real-time quality control (PBRTQC) is essential for clinical laboratory quality management.
  • Traditional PBRTQC relies on statistical methods for monitoring patient test data.
  • Rapid advancements in artificial intelligence (AI) offer new opportunities for PBRTQC innovation.

Purpose of the Study:

  • To systematically review the progress of AI-driven PBRTQC algorithm optimization.
  • To detail the clinical applications of AI-PBRTQC monitoring platforms.
  • To analyze challenges and explore future trends in AI-PBRTQC implementation.

Main Methods:

  • Systematic review of AI-driven PBRTQC algorithm advancements.
  • Detailed account of clinical applications of AI-PBRTQC.
  • In-depth analysis of implementation challenges and future development trends.

Main Results:

  • AI is transitioning PBRTQC from traditional statistics to intelligent monitoring.
  • AI-PBRTQC applications include early warning, cross-setting monitoring, precise control, and anomaly correction.
  • Key challenges include technical limitations, talent shortages, system compatibility, and regulatory lags.

Conclusions:

  • AI significantly optimizes PBRTQC, enhancing clinical laboratory quality management.
  • Addressing implementation challenges is crucial for the widespread adoption of AI-PBRTQC.
  • Future trends point towards further intelligent upgrades and broader application of AI in PBRTQC.