Lorde Nathan, Mahapatra Shivani, Kalaria Tejas
Blood Sciences, Black Country Pathology Services, The Royal Wolverhampton NHS Trust, Wolverhampton WV10 0QP, UK.
Diagnostics (Basel). 2024 Aug 20;14(16):1808. doi: 10.3390/diagnostics14161808.
The rapidly evolving field of machine learning (ML), along with artificial intelligence in a broad sense, is revolutionising many areas of healthcare, including laboratory medicine. The amalgamation of the fields of ML and patient-based real-time quality control (PBRTQC) processes could improve the traditional PBRTQC and error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-systematic errors, and combinations of different types of errors in clinical laboratories. The studies discussed used ML for detecting bias, the requirement for re-calibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validators or traditional PBRTQC algorithms. Advantages, limitations, the creation of standardised ML models, ethical and regulatory aspects and potential future developments have also been discussed in brief.
机器学习（ML）这一快速发展的领域，连同广义上的人工智能，正在彻底改变医疗保健的许多领域，包括检验医学。ML领域与基于患者的实时质量控制（PBRTQC）流程的融合，可以改进实验室中传统的PBRTQC和错误检测算法。这篇叙述性综述讨论了已发表的关于在临床实验室中使用ML检测系统误差、非系统误差以及不同类型误差组合的研究。所讨论的研究通过将ML模型与人工验证者或传统PBRTQC算法的性能进行比较，使用ML来检测偏差、重新校准的需求、被静脉输液或乙二胺四乙酸（EDTA）污染的样本、样本分析延迟、采血管错误、干扰或不同类型误差的组合。还简要讨论了优势、局限性、标准化ML模型的创建、伦理和监管方面以及潜在的未来发展。
