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

Journal: Expert opinion on drug safety

PMID: 38597245

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.

Authors

Harriet Dickinson

Gilead Sciences, Uxbridge, UK. Harriet.dickinson@gilead.com.
Jan Feifel

Merck Healthcare KGaA, Darmstadt, Germany.
Katoo Muylle

Real World Evidence, AstraZeneca Belux, Groot-Bijgaarden, Belgium.
Taichi Ochi

Department of PharmacoTherapy, Epidemiology and Economics, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands.
Enriqueta Vallejo-Yagüe

AstraZeneca, Gaithersberg, MD, USA.

Keywords

Artificial Intelligence Decision Support Systems, Clinical Drug Interactions Drug Labeling Drug-Related Side Effects and Adverse Reactions Humans Machine Learning Pharmacoepidemiology Practice Patterns, Physicians' Precision Medicine

External Resources

View on PubMed Access via DOI PubMed (38597245)

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

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals