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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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精度和进步:机器学习在整形外科的进步.

Mohd Altaf Mir1, Rajesh Maurya1

  • 1Burns and Plastic Surgery, All India Institute of Medical Sciences, Bathinda, Punjab, IND.

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|August 17, 2023
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概括

机器学习 (ML) 通过改善手术前规划,预测结果和个性化治疗来增强整形手术. 挑战包括数据质量和最佳患者护理的伦理考虑.

科学领域:

  • 整形外科 整形外科 整形外科
  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能

背景情况:

  • 机器学习 (ML) 提供了强大的数据分析能力,适用于各种医疗领域.
  • 专注于重建和美学增强的整形外科手术可以从先进的计算工具中受益.
  • 将ML整合到整形外科有望改进手术并改善患者的治疗结果.

研究的目的:

  • 提供ML应用的概述,在整形外科的好处和挑战.
  • 突出ML在手术前评估,手术规划和结果预测中的作用.
  • 讨论ML在面部分析和整形外科内临床数据库管理中的潜力.

主要方法:

  • 分析大型数据集,包括患者图像和临床数据.
  • 应用ML算法用于模式识别,风险因素识别和结果预测.
  • 使用ML用于面部地标检测,对称性评估和结果模拟.

主要成果:

  • 机器学习算法可以预测手术结果,优化技术,并最大限度地减少并发症.
  • 面部识别 ML 助力详细的面部分析和个性化治疗计划.
  • 临床数据库的自动化分析为患者群体和外科手术趋势提供了洞察力.
关键词:
机器倾斜的倾斜方式结果分析结果分析.在手术前进行计划.机器人手术手术程序中的机器人手术程序伤的外观,痕的外观.

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结论:

  • ML显著提高了整形手术决策,患者安全和护理质量.
  • 关键的挑战包括确保数据质量,算法可解释性,以及解决伦理和监管方面的问题.
  • 持续整合ML为推进整形外科实践提供了巨大的潜力.