How Does AI Support Quality Control in Clinical Labs?

Author: Rasit Dinc

Clinical laboratories are the cornerstone of modern healthcare, providing critical data for diagnosis, treatment, and disease prevention. The accuracy and reliability of laboratory results are paramount, making robust quality control (QC) processes essential. However, with an ever-increasing volume of tests and the growing complexity of diagnostic technologies, traditional QC methods are facing unprecedented challenges. The emergence of artificial intelligence (AI) presents a transformative opportunity to enhance and automate quality control in clinical laboratories, ensuring higher accuracy, efficiency, and patient safety.

AI-Powered, Evidence-Based Laboratory Medicine

The integration of AI into laboratory medicine is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care [1]. AI technologies, including machine learning (ML), natural language processing (NLP), and computer vision, are advancing evidence-based laboratory medicine (EBLM) by automating and optimizing critical processes. These processes include formulating clinical questions, conducting literature searches, appraising evidence, and developing clinical guidelines. By automating these tasks, AI reduces the time required for systematic reviews, ensures consistency in evidence appraisal, and enables real-time updates to clinical guidelines [1].

Furthermore, AI supports personalized medicine by analyzing large datasets, including genetic information and electronic health records (EHRs), to tailor diagnostic and treatment plans to individual patient profiles. Predictive analytics, powered by AI, enhance patient outcomes by leveraging historical data and ongoing monitoring to predict responses to treatment and optimize care pathways [1].

Machine Learning for Real-Time Quality Control (PBRTQC)

Machine learning, a subset of AI, is proving to be particularly valuable in the realm of real-time quality control. The amalgamation of ML and patient-based real-time quality control (PBRTQC) processes has the potential to significantly improve traditional PBRTQC and error detection algorithms in the laboratory [2].

ML models can be trained to detect a wide range of errors, including systematic errors (such as instrument bias or reagent degradation), non-systematic errors (such as sample contamination), and combinations of different error types. By comparing the performance of ML models with human validators or traditional PBRTQC algorithms, studies have shown that ML can effectively identify issues such as the need for recalibration, samples contaminated with intravenous fluids or EDTA, delayed sample analysis, and wrong-blood-in-tube errors [2].

One of the key advantages of ML-based PBRTQC is its ability to detect newly introduced bias or imprecision in real-time, which is often superior to periodic internal quality control (IQC) checks. This is particularly useful for analytes that are difficult to keep in control for prolonged periods. However, it is important to note that ML-based PBRTQC is not a panacea. It may not be suitable for all tests, such as low-throughput, qualitative, or semi-quantitative assays. Additionally, the models must be carefully designed to avoid unnecessary interruptions due to random variations in patient data [2].

Challenges and Considerations

Despite its immense potential, the adoption of AI in clinical laboratories is not without its challenges. The accuracy, transparency, and explainability of AI algorithms are critical for gaining the trust of laboratory professionals and ensuring the ethical deployment of these technologies. Integrating AI into existing clinical workflows requires close collaboration between AI developers and laboratory staff to ensure seamless and user-friendly adoption [1].

Ethical considerations, such as data privacy, security, and the potential for algorithmic bias, must be carefully addressed to mitigate risks and ensure equitable healthcare delivery. Regulatory frameworks, such as the EU AI Regulation, are emerging to provide guidance on the development and deployment of AI systems, with a particular emphasis on transparency, data governance, and human oversight for high-risk applications [1].

Economic and Operational Impacts

The economic and operational benefits of implementing AI in clinical laboratory QC are significant. These benefits include cost savings resulting from reduced manual labor and fewer errors, improved diagnostic precision leading to better patient outcomes, and enhanced overall efficiency of laboratory operations [1].

Conclusion

Artificial intelligence is poised to revolutionize quality control in clinical laboratories. From enhancing evidence-based laboratory medicine to enabling real-time error detection through machine learning, AI offers a powerful set of tools to address the challenges of modern laboratory practice. While there are challenges to overcome, the potential benefits of AI in terms of improved accuracy, efficiency, and patient safety are undeniable. As AI technologies continue to evolve, we can expect to see even more innovative applications that will further transform the field of laboratory medicine, paving the way for a new era of precision and personalized healthcare.

References

[1] Pillay, T. S., Topcu, D. İ., & Yenice, S. (2025). Harnessing AI for enhanced evidence-based laboratory medicine (EBLM). Clinica Chimica Acta, 569, 120181. https://doi.org/10.1016/j.cca.2025.120181

[2] Lorde, N., Mahapatra, S., & Kalaria, T. (2024). Machine Learning for Patient-Based Real-Time Quality Control (PBRTQC), Analytical and Preanalytical Error Detection in Clinical Laboratory. Diagnostics (Basel, Switzerland), 14(16), 1808. https://doi.org/10.3390/diagnostics14161808