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相关概念视频

Pharmacovigilance01:19

Pharmacovigilance

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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.
In some cases, there...
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Drug Regulation01:25

Drug Regulation

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Drug regulation encompasses the management of drug usage by evaluating its safety and efficacy through assessments conducted by regulatory authorities. Regrettably, the history of drug regulation is marred by several catastrophic events. One such incident is the Elixir Sulfanilamide tragedy, in which the toxic compound diethyl glycol was included in a sweet-tasting medication, leading to numerous fatalities. This event prompted the enactment of the Food, Drug, and Cosmetic Act in 1938. Under...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

237
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Blind Procedures02:07

Blind Procedures

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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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Clinical Trials: Overview01:11

Clinical Trials: Overview

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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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Updated: Jun 11, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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算法监管,从药物监管中吸取的教训

Alan Balendran1, Mehdi Benchoufi2, Theodoros Evgeniou3

  • 1Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France. alan.balendran@u-paris.fr.

NPJ digital medicine
|October 2, 2024
PubMed
概括
此摘要是机器生成的。

医疗保健中的人工智能 (AI) 系统需要严格的监控. 调整药监方法可以提高AI的安全性,并减轻部署后事件的风险.

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能的人工智能
  • 公共卫生 公共卫生

背景情况:

  • 人工智能 (AI) 系统越来越多地用于医疗保健等高风险行业.
  • 尽管有评估工作,但人工智能系统在部署后遇到事件,这给缓解带来了重大挑战.
  • 现有的药物安全框架,称为药物监测,为监测现实世界的表现提供了先例.

研究的目的:

  • 探索药物监测原则的适应,以监测医疗保健中的AI系统.
  • 加强应对与人工智能部署相关的不利影响和风险.
  • 为改善AI在医疗保健和其他领域的安全协议提供基础.

主要方法:

  • 药物监督原则的概念性调整.
  • 关于人工智能安全和药物安全监测的文献综述.
  • 讨论人工智能系统监控的潜在框架.

主要成果:

  • 药监监测为人工智能系统监控提供了一个可转移的模型.
  • 诸如不良事件检测和风险评估等关键概念可以应用于AI.
  • 提出了一种结构化的方法来管理与AI相关的事件.

结论:

  • 调整药监是确保医疗保健中人工智能安全的一个有希望的策略.
  • 这种方法可以更有效地识别和减轻AI相关风险.
  • 拟议的框架对超越医疗保健部门的人工智能安全有影响.