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相关概念视频

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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机器学习,深度学习,人工智能和美容整形手术:定性系统性审查

Raquel Nogueira1, Marina Eguchi2, Julia Kasmirski3

  • 1Department of Surgery, Montefiore Medical Center, 1825 Eastchester Rd, Bronx, NY, 10461, USA. raquelnogueiramd@gmail.com.

Aesthetic plastic surgery
|October 9, 2024
PubMed
概括

机器学习 (ML),深度学习 (DL) 和人工智能 (AI) 在美容整形手术中显示出巨大的潜力. 这些技术可以优化治疗和预测并发症,但需要谨慎使用,以管理患者的期望.

关键词:
整形外科整形美容手术人工智能的人工智能是人工智能.深度学习是一种深度学习.机器学习 机器学习整形外科手术 整形外科手术

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算机辅助手术 计算机辅助手术
  • 整形外科 整形外科 整形外科

背景情况:

  • 先进的计算技术的整合在各种医疗学科中正在迅速发展.
  • 审美整形手术正在越来越多地探索创新工具,以提高患者的治疗结果和手术精度.

研究的目的:

  • 系统地审查机器学习 (ML),深度学习 (DL) 和人工智能 (AI) 在美容整形手术中的应用.
  • 评估这些技术对手术决策和并发症预测的现状和潜在影响.

主要方法:

  • 根据PRISMA指南进行了定性系统审查.
  • 在MEDLINE/PubMed,EMBASE和Cochrane图书馆进行了搜索,使用与ML,DL,AI和整形外科相关的术语.
  • 使用ROBINS-I工具对包含的非随机化研究进行偏差风险评估.

主要成果:

  • 在2019年至2024年间发表的18项研究符合纳入标准.
  • 应用涵盖了各种程序,包括乳房增大,鼻整形,面部复发和身体轮.
  • 基于图像的AI,ML和DL算法被用于改善决策和识别影响术后并发症的因素.

结论:

  • 人工智能,ML和DL算法对改变美容整形手术具有重大前景.
  • 这些技术可以帮助优化治疗计划,预测并发症,并澄清患者的担忧.
  • 仔细实施至关重要,以避免设置不切实际的患者期望,强调保守的外科沟通的持续重要性.