数字时代的血液病实践:发生了什么变化?

Olga Pozdnyakova
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引用次数: 0

摘要

血液病理学工作流程是复杂的,因为它们包括指导进一步测试、诊断和患者管理所需的大量数据点。工作流程从全血细胞计数开始,随后对外周血(PB)和骨髓(BM)进行形态学评估。通过实施人工智能辅助和自动化评估,数字病理学有可能彻底改变PB和BM评估,但实现这一最终目标仍存在主要障碍,例如缺乏监管、数据标准化、知识和培训不足以及对变革的抵制等。本文回顾了数字化在血液病理学实践中的现状,最近使用机器学习模型进行自动标本分析的研究,概述了人工智能在临床应用中面临的优势和障碍,并提供了人工智能驱动的临床工作流程,以实现高效和全面的临床检查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hematopathology Practice in the Digital Era: What has Changed?

Hematopathology workflows are complex, since they include numerous data points necessary for guiding further testing, diagnosis, and patient management. The workflows start with complete blood cell counts, with subsequent morphologic evaluation of peripheral blood (PB) and bone marrow (BM). Digital pathology has the potential to revolutionize PB and BM assessment through the implementation of artificial intelligence for assisted and automated evaluation, but there remain major hurdles toward this ultimate goal, such as lack of regulatory oversight, data standardization, insufficient knowledge and training, and resistance to change, among others. This article reviews the current state of digitalization in the hematopathology practice, recent research using machine learning models for automated specimen analysis, outlines the advantages and barriers facing clinical implementation of artificial intelligence, and offers prospective artificial intelligence-driven clinical workflows for efficient and comprehensive clinical workup.

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