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

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.

EXPERT COMMENTARY SUMMARY

Artificial intelligence or machine learning (AI/ML) based systems that guide the prescription of medications have the potential to vastly improve patient care, but these tools should only provide recommendations that are in line with the label of a medicine. With a constantly evolving medication label, this is likely to be a challenge, and this also has implications for the off-label use of medicines.

KEYWORDS:

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

Article highlights

• Artificial intelligence or machine learning (AI/ML) based systems that guide the prescription of medications have the potential to vastly improve patient care by personalizing treatment decisions to individual patients.

• These tools need to help healthcare workers prescribe in a safe manner, and hence they need to be aware of the ‘label’ of a medicine (i.e. the instructions of how and when to use a specific medicine).

• The label of a medication can change rapidly, especially when there is an important safety concern. These rapid changes may prove challenging to AI-based tools that inform or drive prescription, which could be slower to respond to new guidance.

• These tools may also restrict or discourage the use of medicines in specific populations (for example, in pregnancy, where based on the precautionary principle many medicines advise against prescription).

• There is a clear mandate for co-operation between software creators, healthcare practitioners, pharmacoepidemioloigists, and regulators in monitoring the safe use of these tools.

Declaration of interests

H Dickinson is an employee and stockholder in Gilead Sciences Inc. J Feifel is an employee and stockholder of Merck KGaA. K Muylle is an employee of AstraZeneca BeLux. E Vallejo-Yagüe and T Ochi have no financial incentives to disclose. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or material discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Data availability statement

Data sharing not applicable – no new data generated.

Additional information

Funding

This paper was not funded.