机器学习和基于人工智能的临床决策支持,用于现代血液学
Cindy Zhang1, Barbara D Lam2, Fabienne Lucas1
1Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
Clinics in laboratory medicine
|October 29, 2025
概括
机器学习 (ML) 正在改变血液学诊断. 本综述探讨了医疗保健专业人员在血液学子领域的ML应用,成功和局限性.
科学领域:
- 医疗信息学 医疗信息学
- 临床病理学 临床病理学
- 计算生物学 计算生物学
背景情况:
- 血液学是一个数据丰富的医学领域.
- 技术创新正在迅速推进血液学.
- 机器学习 (ML) 在诊断中的整合正在增加.
研究的目的:
- 审查目前的ML研究和血液学临床应用的现状.
- 为医疗保健专业人员提供有关ML对工作流程的影响的信息.
- 探索血液学中ML的成功和局限性.
主要方法:
- 在血液学中对ML的文献综述.
- 对各种血液学子领域的研究进行分析.
- 检查临床应用和部署.
主要成果:
- 在各种血液学领域,ML显示出显著的潜力.
- 在血液病理学,血红蛋白病变和凝血病变方面注意到了成功.
- 确定了ML研究和部署中的局限性和挑战.
结论:
- 了解ML对于血液学护理团队至关重要.
- ML工具需要仔细整合到临床工作流程中.
- 需要进一步的研究来解决血液学中的ML限制.
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