Rashidi Hooman H, Tran Nam, Albahra Samer, Dang Luke T
Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA, USA.
Int J Lab Hematol. 2021 Jul;43 Suppl 1:15-22. doi: 10.1111/ijlh.13537.
Artificial Intelligence (AI) and machine learning (ML) have now spawned a new field within health care and health science research. These new predictive analytics tools are starting to change various facets of our clinical care domains including the practice of laboratory medicine. Many of these ML tools and studies are also starting to populate our literature landscape as we know it but unfamiliarity of the average reader to the basic knowledge and critical concepts within AI/ML is now demanding a need to better prepare our audience to such relatively unfamiliar concepts. A fundamental knowledge of such platforms will inevitably enhance cross-disciplinary literacy and ultimately lead to enhanced integration and understanding of such tools within our discipline. In this review, we provide a general outline of AI/ML along with an overview of the fundamental concepts of ML categories, specifically supervised, unsupervised, and reinforcement learning. Additionally, since the vast majority of our current approaches within ML in laboratory medicine and health care involve supervised algorithms, we will predominantly concentrate on such platforms. Finally, the need for making such tools more accessible to the average investigator is becoming a major driving force for the need of automation within these ML platforms. This has now given rise to the automated ML (Auto-ML) world which will undoubtedly help shape the future of ML within health care. Hence, an overview of Auto-ML is also covered within this manuscript which will hopefully enrich the reader's understanding, appreciation, and the need for embracing such tools.
人工智能（AI）和机器学习（ML）如今已在医疗保健和健康科学研究领域催生了一个新领域。这些新的预测分析工具正开始改变我们临床护理领域的各个方面，包括检验医学实践。正如我们所知，许多这类机器学习工具和研究也开始充斥于我们的文献领域，但普通读者对人工智能/机器学习中的基础知识和关键概念并不熟悉，现在需要让我们的受众更好地了解这些相对陌生的概念。对这类平台的基本知识必然会提高跨学科素养，并最终增强我们学科对这些工具的整合与理解。在本综述中，我们提供了人工智能/机器学习的总体概述，以及机器学习类别（特别是监督学习、无监督学习和强化学习）的基本概念概述。此外，由于我们目前在检验医学和医疗保健领域的机器学习方法绝大多数都涉及监督算法，我们将主要关注此类平台。最后，让普通研究人员更容易使用这些工具的需求正成为这些机器学习平台实现自动化的主要驱动力。这就催生了自动化机器学习（Auto - ML）领域，它无疑将有助于塑造医疗保健领域机器学习的未来。因此，本手稿还涵盖了对自动化机器学习的概述，希望能丰富读者对这类工具的理解、欣赏，并认识到采用它们的必要性。
