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Artificial Intelligence in Pharmacovigilance: Scoping Points to Consider.

Manfred Hauben1, Craig G Hartford2

  • 1Safety Sciences Research, Pfizer Inc, New York, NY, USA; Department of Medicine, NYU Langone Health, New York, NY, USA.

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|January 22, 2021
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Summary
This summary is machine-generated.

Artificial intelligence in pharmacovigilance (AIPV) is growing. This article proposes a working definition for AIPV, clarifying its scope, methods, and applications for better understanding and implementation.

Keywords:
AIartificial intelligencemachine learningpharmacovigilancetechnology

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Area of Science:

  • Pharmacovigilance
  • Artificial Intelligence
  • Drug Safety

Background:

  • Artificial intelligence (AI) is increasingly integrated into pharmacovigilance (PV).
  • A clear definition of AI in PV (AIPV) is needed to delineate its scope.
  • Understanding AIPV scope is crucial for defining terms, methods, tasks, and data sets.

Purpose of the Study:

  • To explore key considerations for defining the scope of AI in pharmacovigilance (AIPV).
  • To propose a working definition for the scope of AIPV.
  • To enhance clarity and consistency in the application of AI within pharmacovigilance.

Main Methods:

  • Literature review of AI applications in pharmacovigilance.
  • Analysis of current practices and terminology in AIPV.
  • Development of a conceptual framework for defining AIPV scope.

Main Results:

  • Identified key factors influencing the scope of AIPV.
  • Proposed a working definition for AIPV.
  • Highlighted the interdisciplinary nature of AIPV.

Conclusions:

  • A defined scope for AIPV is essential for its effective application.
  • The proposed definition aids in standardizing terms, methods, and data usage in AIPV.
  • Further research and consensus are needed to refine the scope of AI in pharmacovigilance.