Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels

Harriet Dickinson*, Jan Feifel, Katoo Muylle, Taichi Ochi, Enriqueta Vallejo-Yagüe

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Introduction: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the “label” of the medicines involved. The label of a medicine is an approved guide that indicates how to prescribe the drug in a safe and effective manner. Areas covered: The label for a medicine may evolve as new information on drug safety and effectiveness emerges, leading to the addition or removal of warnings, drug-drug interactions, or to permit new indications. However, the speed at which these updates are made to these AI/ML recommendation systems may be delayed and could influence the safety of prescribing decisions. This article explores the need to keep AI/ML tools ‘in sync’ with any label changes. Additionally, challenges relating to medicine availability and geographical suitability are discussed. Expert Opinion: These considerations highlight the important role that pharmacoepidemiologists and drug safety professionals must play within the monitoring and use of these tools. Furthermore, these issues highlight the guiding role that regulators need to have in planning and oversight of these tools.

Original languageEnglish
Pages (from-to)547–552
Number of pages6
JournalExpert Opinion on Drug Safety
Volume23
Issue number5
DOIs
Publication statusPublished - 6-May-2024

Keywords

  • Artificial intelligence
  • CDS clinical decision support
  • drug label
  • machine learning
  • precision medicine
  • SaMD

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