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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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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Orchestrating generative AI in pharmacovigilance: predicting and preempting the unpredictable.

Darmendra Ramcharran1, Jeffery L Painter2, Vijay Kara3

  • 1GSK, 1000 Winter Street, Waltham, MA 02451, USA University of Rhode Island, Kingston, RI, USA.

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Summary

Generative artificial intelligence (GenAI) presents new opportunities and challenges for pharmacovigilance (PV). This review explores GenAI integration in drug and vaccine safety, proposing a framework for responsible implementation.

Keywords:
drug safetygenerative AIpharmacovigilance

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Area of Science:

  • Pharmacovigilance and Artificial Intelligence

Background:

  • Generative artificial intelligence (GenAI) is rapidly emerging as a transformative technology.
  • Pharmacovigilance (PV) faces evolving challenges in drug and vaccine safety monitoring.
  • Integrating advanced AI into high-risk safety domains requires careful consideration.

Purpose of the Study:

  • To review emerging trends and practical applications of GenAI in pharmacovigilance.
  • To examine the potential benefits and inherent risks of GenAI in drug and vaccine safety.
  • To propose a framework for the ethical and effective integration of GenAI into PV systems.

Main Methods:

  • This perspective review synthesizes current experimental data and early real-world applications.
  • It analyzes conceptual frameworks for GenAI adoption in pharmacovigilance.
  • The review emphasizes rigorous testing, human oversight, and ethical considerations.

Main Results:

  • GenAI offers significant opportunities for enhancing pharmacovigilance processes.
  • Potential risks include data privacy, algorithmic bias, and the need for robust validation.
  • A structured framework is proposed to guide GenAI implementation in PV.

Conclusions:

  • GenAI can revolutionize pharmacovigilance if implemented thoughtfully.
  • Successful integration necessitates a focus on safety, ethics, and human-AI collaboration.
  • This work supports PV professionals in navigating the evolving landscape of AI in drug safety.