General Directorate of Health Services, Rare Diseases Department, Turkish Ministry of Health, Ankara, Türkiye.
Hacettepe University Institute of Informatics, Ankara, Türkiye.
Clin Chem Lab Med. 2023 Nov 29;62(5):793-823. doi: 10.1515/cclm-2023-1037. Print 2024 Apr 25.
Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.
人工智能（AI）和机器学习（ML）在医学实验室和更广泛的医疗保健领域变得至关重要。在这篇综述文章中，我们总结了 ML 模型的发展，以及它们如何有助于临床实验室工作流程并改善患者预后。ML 模型的开发过程涉及数据收集、数据清理、特征工程、模型开发和优化。这些模型一旦最终确定，就需要进行彻底的性能评估和验证。最近，由于模型开发的复杂性，自动化的 ML 工具也被引入以简化流程，使非专业人员也能够创建模型。临床决策支持系统（CDSS）使用 ML 技术对大型数据集进行分析，以帮助医疗保健专业人员解释检测结果。它们正在彻底改变医学实验室，使实验室在分析前、分析中和分析后各个阶段能够在更少的人工监督下更高效地工作。尽管 ML 工具在所有分析阶段都有贡献，但它们的集成也带来了一些挑战，如潜在的模型不确定性、黑盒算法以及专业技能的削弱。此外，获取多样化的数据集很困难，并且模型的复杂性可能会限制其临床应用。总之，医疗保健中的基于 ML 的 CDSS 可以极大地增强临床决策。然而，要成功采用这些工具，需要专业人员和利益相关者之间的合作，利用混合智能、外部验证和性能评估。
