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

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.