概化理论
计算机科学
工作流程
软件部署
人工智能
过程(计算)
机器学习
数据科学
软件工程
心理学
操作系统
发展心理学
数据库
作者
Robert Seifert,David Gorlin,Andrew Borkowski
标识
DOI:10.1016/j.cll.2023.04.009
摘要
In this review, the authors discuss the fundamental principles of machine learning. They explore recent studies and approaches in implementing machine learning into flow cytometry workflows. These applications are promising but not without their shortcomings. Explainability may be the biggest barrier to adoption, as they contain "black boxes" in which a complex network of mathematical processes learns features of data that are not translatable into real language. The authors discuss the current limitations of machine learning models and the possibility that, without a multiinstitutional development process, these applications could have poor generalizability. They also discuss widespread deployment of augmented decision-making.
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