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

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.