Artificial intelligence algorithm optimization and application in patient-based real-time quality control (PBRTQC)
Clinical Snapshot
PICO Framework
| P — Population | Clinical laboratories implementing quality control systems |
| I — Intervention | AI-driven patient-based real-time quality control (AI-PBRTQC) algorithms |
| C — Comparator | Traditional statistics-driven PBRTQC models |
| O — Outcomes | Quality control performance, early warning capabilities, anomaly detection, system compatibility |
Bottom Line
This narrative review examines the application of artificial intelligence in patient-based real-time quality control systems for clinical laboratories. While it provides a broad overview of AI-PBRTQC applications including early warning systems and anomaly detection, the review lacks systematic methodology. The authors identify important implementation challenges including technical limitations, workforce requirements, and regulatory gaps. However, without systematic literature search, quality assessment, or quantitative synthesis, the evidence base remains unclear. For Australian laboratories, any AI-PBRTQC implementation would require careful consideration of NPAAC quality standards and TGA regulatory requirements. The review serves as an introductory overview but cannot provide definitive guidance for practice change due to methodological limitations.
Key Findings
Effect Size: Not quantified
Primary Outcome: AI-PBRTQC applications in clinical laboratories
Nnt Or Sensitivity: Not reported
Confidence Interval: Not provided
Clinical Application
Implementation challenges identified including technical limitations and regulatory barriers Relevant to Australian pathology services under NPAAC guidelines, though specific local validation would be required for TGA compliance Clinical laboratories seeking to implement or upgrade quality control systems
Abstract
Patient-based real-time quality control (PBRTQC) serves as a vital supplement to quality management in clinical laboratories. Its core principle is to monitor the testing process in real time and continuously through patient test data. As artificial intelligence (AI) technology develops rapidly, AI has provided novel pathways for the innovation of PBRTQC algorithms and drives its transition from a traditional statistics-driven model to intelligent monitoring. This review systematically summarizes the progress of AI-driven PBRTQC algorithm optimization. Meanwhile, it provides a detailed account of the clinical applications of the AI-PBRTQC monitoring platform. These applications encompass timely quality control early warning, homogeneous monitoring across multiple settings, precise quality control in complex clinical settings, anomaly traceability and subsequent correction. In addition, this review offers an in-depth analysis of the challenges that arise during the practical implementation of AI-PBRTQC. These include technical limitations, shortage of professional talents, system compatibility barriers, and lagging standardization and regulation. It also explores future development trends and provides valuable references for the intelligent upgrade of PBRTQC.
References
- 1.Su, B., Zhang, Y., & Shi, X. (2026). Artificial intelligence algorithm optimization and application in patient-based real-time quality control (PBRTQC). Clinica Chimica Acta, 120946. https://doi.org/10.1016/j.cca.2026.120946
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