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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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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
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FDA Approved Drugs: Changes to Approved Drugs01:26

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Body:Biologics, derived from living sources such as humans, animals, or microorganisms, represent a significant category of pharmaceuticals. These complex molecules, developed through advanced biotechnological methods or purified from natural sources, include essential medical treatments like insulin and growth hormones. The complexity of biologics arises from their large molecular structures and the intricate processes required for their production, making them distinct from conventional...
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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对FDA批准的人工智能医疗器械的利益风险报告.

John C Lin1, Bhav Jain2, Jay M Iyer3

  • 1Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia.

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美国食品和药物管理局 (FDA) 对人工智能 (AI) 和机器学习 (ML) 设备的许可缺乏标准化的有效性和安全性报告. 需要改善AI/ML医疗器械的监管途径和市场后监管.

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科学领域:

  • 医疗器械监管法规 医疗器械监管法规
  • 医疗保健中的人工智能
  • 机器学习在医学中的应用

背景情况:

  • 人工智能 (AI) 和机器学习 (ML) 设备在临床环境中越来越多地被采用.
  • 美国食品和药物管理局 (FDA) 对这些AI/ML设备的益处风险评估和监测存在担忧.

研究的目的:

  • 描述FDA批准的AI/ML设备的市场前和市场后疗效,安全性和风险评估报告.

主要方法:

  • 一项横截面研究分析了相关的FDA数据,用于从1995年9月到2023年7月清除的AI/ML设备.
  • 数据来源包括FDA决策摘要,批准数据库和不良事件和召回数据库.

主要成果:

  • 在到2023年清除的691个AI/ML设备中,许多设备缺乏关于研究设计 (46.7%),培训数据 (53.3%) 和人口统计数据 (95.5%) 的报告.
  • 很少有设备报告了随机试验 (1.6%) 或前性研究 (7.7%) 的数据.
  • 对5.2%的设备报告了不良事件,其中5.8%的设备被召回,主要是由于软件问题.

结论:

  • 目前缺乏FDA批准的AI/ML设备的标准化疗效,安全性和风险评估报告.
  • 推专门的监管途径和加强市场后监管,以应对AI/ML设备安全方面的挑战.