Background Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine. Content In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine. Summary AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.

中文翻译：

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人工智能和绘制检验医学的新方向：回顾

背景 现代人工智能 (AI) 和机器学习 (ML) 方法现在能够完成具有与专业人类操作员相当的性能特征的任务。因此，整个医疗保健领域的许多领域都在采用这些技术，包括体外诊断，以及更广泛的实验室医学。然而，关于 AI/ML 在检验医学中应用的前景、可能的未来和挑战的文献综述有限。内容在这篇评论中，我们首先简要介绍了人工智能及其机器学习的子领域。接下来的部分描述了目前在临床实验室实践中或在最近的文献中被提议用于此类用途的 ML 系统、在临床实验室之外使用实验室数据的 ML 系统、对采用 ML 的挑战、以及实验室医学中 ML 的未来机会。总结 AI 和 ML 已经并将继续显着影响检验医学的实践和范围。现代计算的进步和健康信息的广泛数字化使这成为可能。这些技术正在迅速开发和描述，但相比之下，迄今为止它们的实施情况并不大。为了促进可靠和复杂的基于机器学习的技术的实施，我们需要进一步建立最佳实践并改进我们的信息系统和通信基础设施。临床实验室社区的参与对于确保实验室数据充分可用并认真纳入稳健、安全和临床有效的 ML 支持的临床诊断中至关重要。