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Related Concept Videos

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.
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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 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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Algorithmovigilance, lessons from pharmacovigilance.

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.

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Summary
This summary is machine-generated.

Artificial Intelligence (AI) systems in healthcare require robust monitoring. Adapting pharmacovigilance methods can improve AI safety and mitigate risks from post-deployment incidents.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Public Health

Background:

  • Artificial Intelligence (AI) systems are increasingly used in high-risk sectors like healthcare.
  • Despite evaluation efforts, AI systems experience post-deployment incidents, posing significant challenges for mitigation.
  • Existing frameworks for drug safety, known as pharmacovigilance, offer a precedent for monitoring real-world performance.

Purpose of the Study:

  • To explore the adaptation of pharmacovigilance principles for monitoring AI systems in healthcare.
  • To enhance the response to adverse effects and risks associated with AI deployment.
  • To provide a foundation for improved safety protocols for AI in healthcare and other domains.

Main Methods:

  • Conceptual adaptation of pharmacovigilance principles.
  • Literature review of AI safety and drug safety monitoring.
  • Discussion of potential frameworks for AI system monitoring.

Main Results:

  • Pharmacovigilance offers a transferable model for AI system monitoring.
  • Key concepts like adverse event detection and risk assessment can be applied to AI.
  • Proposes a structured approach to managing AI-related incidents.

Conclusions:

  • Adapting pharmacovigilance is a promising strategy for ensuring AI safety in healthcare.
  • This approach can lead to more effective identification and mitigation of AI-related risks.
  • The proposed framework has implications for AI safety beyond the healthcare sector.