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Next-Generation Patient-Based Real-Time Quality Control Models.

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Summary
This summary is machine-generated.

Patient-based real-time quality control (PBRTQC) leverages AI for assay performance assessment. This review critically compares AI-driven PBRTQC methods against traditional approaches, highlighting AI

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Area of Science:

  • Clinical Chemistry
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Patient-based real-time quality control (PBRTQC) monitors laboratory assay performance using patient data.
  • Advancements in computer science and computing power have driven PBRTQC algorithm development.
  • Artificial Intelligence (AI) has seen rapid adoption in PBRTQC, promising significant advantages.

Purpose of the Study:

  • To critically compare various PBRTQC algorithms, including AI-based methods.
  • To contrast AI-driven PBRTQC with conventional quality control approaches.
  • To evaluate the feasibility and benefits of implementing AI in PBRTQC.

Main Methods:

  • Description and contrast of PBRTQC algorithms: moving averages, regression-adjusted real-time QC, neural networks, and anomaly detection.
  • Comparative analysis of AI-based versus conventional PBRTQC techniques.
  • Assessment of AI tool characteristics relevant to laboratory implementation.

Main Results:

  • AI tools are increasingly available, user-friendly, and computationally efficient for PBRTQC.
  • Traditional disadvantages of AI, such as complexity and high resource needs, are diminishing.
  • AI offers attractive solutions for enhancing PBRTQC.

Conclusions:

  • AI-powered PBRTQC methods offer significant advantages over conventional techniques.
  • The increasing accessibility and efficiency of AI tools facilitate their adoption in clinical laboratories.
  • AI is poised to revolutionize PBRTQC by overcoming limitations of traditional methods.