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Issues And Trends In Healthcare Delivery System01:29

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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.
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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.
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相关实验视频

Updated: Feb 19, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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医学教育中的人工智能:变革潜力,当前应用和未来影响

Juan S Izquierdo-Condoy1, Marlon Arias-Intriago1, Laura Montero Corrales2

  • 1One Health Research Group, Universidad de Las Américas, Via Nayon S/N, Quito, 170124, Ecuador, +593 995760693.

JMIR medical education
|February 17, 2026
PubMed
概括

人工智能 (AI) 通过适应性学习和人工智能辅助工具增强医学教育. 负责任的整合需要解决道德问题,并确保公平获得更好的学习成果.

关键词:
适应性学习系统适应性学习系统人工智能的人工智能是人工智能.未来的影响 未来的影响机器学习是机器学习.医学教育 医学教育

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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科学领域:

  • 医疗教育 技术 技术 医学教育
  • 医疗保健中的人工智能
  • 数字学习策略 数字学习策略

背景情况:

  • 人工智能 (AI) 正在通过适应性学习,人工智能辅助评估和可扩展工具改变医学教育.
  • 像自然语言处理,机器学习和生成的大型语言模型等技术提供了新的教学和学习支持.
  • 整合人工智能带来了重大的伦理,教学和基础设施挑战.

研究的目的:

  • 检查医疗教育当前的人工智能应用,好处和挑战.
  • 为医疗培训中负责任和有效地整合人工智能提出战略.
  • 为评估人工智能对医学教育的影响提供一个框架.

主要方法:

  • 审查人工智能应用程序,包括聊天机器人,虚拟患者和智能辅导系统.
  • 对AI工具进行评估的分析,例如自动分级和预测分析.
  • 检查人工智能辅助的内容创建工具.

主要成果:

  • 人工智能工具增强了个性化,沉浸式学习,并简化了内容创作.
  • 人工智能通过自动分级和预测分析提供高效的评估方法.
  • 担忧包括数据隐私,算法偏见,不平等的访问,以及对批判性思维的潜在影响.

结论:

  • 人工智能为彻底改变医学教育提供了巨大的机会.
  • 伦理,公平和教学健全的整合对于人工智能采用至关重要.
  • 像FACETS这样的框架,机构治理和协作是成功实施AI的关键.