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In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess...
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人工智能和机器学习在肝脏疾病中的应用.

Bhargav Vemulapalli1, Meghana Ghattu1, Kavya Atluri1

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概括

人工智能 (AI) 和机器学习 (ML) 正在通过整合各种数据来早期诊断和个性化管理来彻底改变肝病护理. 这些进步有望改善各种肝病患者的治疗结果.

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科学领域:

  • 肝病学 肝病学是一种肝病学.
  • 医疗信息学 医疗信息学
  • 人工智能的人工智能

背景情况:

  • 肝病学正在越来越多地利用数据驱动的方法.
  • 整合多样化的患者数据 (临床,实验室,成像,可穿戴设备) 提出了挑战和机遇.
  • 当前的管理策略可以从增强的预测和诊断工具中受益.

研究的目的:

  • 审查AI和ML在肝病学中的变革性影响.
  • 突出早期诊断,风险预测和患者管理中的应用.
  • 讨论AI/ML在主要肝脏疾病中的作用以及未来的方向.

主要方法:

  • 关于人工智能和ML在肝病学中的应用的当前文献的审查.
  • 综合与数据整合和疾病管理相关的发现.
  • 对数字健康和人工智能可解释性趋势的分析.

主要成果:

  • 人工智能和ML正在实现早期诊断和肝脏疾病风险预测.
  • 这些技术增强了个性化的患者护理和非侵入性监测.
  • 应用范围包括与代谢功能障碍相关的脂肪性肝病,肝硬化,C型肝炎,肝移植和肝细胞癌.

结论:

  • 人工智能和机器学习是肝病学的未来不可或缺的一部分.
  • 数字健康和可解释性的持续进步将推动更广泛的采用.
  • 这些技术将大大改善患者的治疗结果和管理策略.