人工智能是如何改变科的
Miguel Hueso1,2, Alfredo Vellido3,4
1Department of Nephrology, Hospital Universitari Bellvitge and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, C/Feixa llarga, s/n, Barcelona, 08907, Spain. mhueso@idibell.cat.
BMC nephrology
|August 27, 2024
概括
人工智能 (AI) 和机器学习 (ML) 为以数据为中心的脏学研究提供了强大的工具,分析复杂的分子系统和患者数据. 然而,将这些人工智能进步转化为临床有用的应用程序需要可解释的方法和解决数字沟.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
背景情况:
- 科研究越来越多地关注复杂的分子系统,病理学和透析和移植等治疗方法.
- 奥米克,传感器和数字设备的进步产生了大量的数据密集型研究.
- 以数据为中心的科学需要强大的计算工具来获得新的生物标志物和治疗点.
研究的目的:
- 提供当前在科人工智能 (AI) 应用的概述.
- 突出AI和机器学习 (ML) 在处理病研究多式联络数据方面的潜力.
- 强调需要临床兼容,可解释的人工智能系统,用于科个性化医疗.
主要方法:
- 审查当前的AI和ML在科研究中的应用.
- 讨论利用多式联络数据,包括基因组学,信号,图像和电子健康记录 (EHR).
- 强调开发可解释和可解释的ML模型用于临床翻译.
主要成果:
- 人工智能和机器学习为以数据为中心的脏病学提供了显著的分析优势.
- 基于ML的决策支持系统中只有很小一部分获得了临床验证.
- 在技术获取方面存在数字沟,影响发展中国家和代表性不足的少数群体.
结论:
- 智能科,使用人工智能以数据为中心的方法,处于早期阶段.
- 开发可解释的AI和明确的分析策略对于临床翻译至关重要.
- 解决技术差异对于在人工智能驱动的脏病学中取得公平的进步至关重要.
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