Department of Digital Medicine, Brain Korea 21 Project, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Department of Biomedical Engineering, Graduate School, Chonnam National University, Yeosu, Korea.
Ann Lab Med. 2025 Jan 1;45(1):22-35. doi: 10.3343/alm.2024.0354. Epub 2024 Nov 26.
Machine learning (ML) is currently being widely studied and applied in data analysis and prediction in various fields, including laboratory medicine. To comprehensively evaluate the application of ML in laboratory medicine, we reviewed the literature on ML applications in laboratory medicine published between February 2014 and March 2024. A PubMed search using a search string yielded 779 articles on the topic, among which 144 articles were selected for this review. These articles were analyzed to extract and categorize related fields within laboratory medicine, research objectives, specimen types, data types, ML models, evaluation metrics, and sample sizes. Sankey diagrams and pie charts were used to illustrate the relationships between categories and the proportions within each category. We found that most studies involving the application of ML in laboratory medicine were designed to improve efficiency through automation or expand the roles of clinical laboratories. The most common ML models used are convolutional neural networks, multilayer perceptrons, and tree-based models, which are primarily selected based on the type of input data. Our findings suggest that, as the technology evolves, ML will rise in prominence in laboratory medicine as a tool for expanding research activities. Nonetheless, expertise in ML applications should be improved to effectively utilize this technology.
机器学习（ML）目前在数据分析和预测的各个领域，包括实验室医学中被广泛研究和应用。为了全面评估 ML 在实验室医学中的应用，我们回顾了 2014 年 2 月至 2024 年 3 月期间发表的关于 ML 在实验室医学中应用的文献。使用搜索字符串在 PubMed 上进行搜索，共获得了 779 篇关于该主题的文章，其中有 144 篇文章被选入本次综述。对这些文章进行分析，以提取和分类实验室医学内的相关领域、研究目标、标本类型、数据类型、ML 模型、评估指标和样本量。使用桑基图和饼图来说明类别之间以及每个类别内的比例关系。我们发现，大多数涉及 ML 在实验室医学中的应用的研究旨在通过自动化提高效率或扩大临床实验室的作用。使用的最常见的 ML 模型是卷积神经网络、多层感知器和基于树的模型，主要根据输入数据的类型进行选择。我们的研究结果表明，随着技术的发展，ML 将作为一种扩展研究活动的工具，在实验室医学中变得更加突出。然而，应该提高 ML 应用方面的专业知识，以有效利用这项技术。
