Will the future of pharmacovigilance be more automated?

ABSTRACT

Introduction

Artificial intelligence (AI) based tools offer new opportunities for pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored to preserve and strengthen medical and pharmacological expertise in drug safety.

Areas covered

This work aims to describe PV tasks in which the contribution of AI and intelligent automation (IA) tools is required, in the context of a continuous increase of spontaneous reporting cases and regulatory tasks. A narrative review with expert selection of pertinent references was performed through Medline. Two areas were covered, management of spontaneous reporting cases and signal detection.

Perspective

The use of AI and IA tools will assist a large spectrum of PV activities, both in public and private PV systems, in particular for tasks of low added value (e.g. initial quality check, verification of essential regulatory information, search for duplicates). Testing, validating, and integrating these tools in the PV routine are the actual challenges for modern PV systems, to guarantee high-quality standards in terms of case management and signal detection.

KEYWORDS:

• Artificial intelligence
• human intelligence
• digital twins
• machine learning
• explainability
• regulation
• system interoperability

Article highlights

• The challenge for the future of PV is to implement tools that can assist human intelligence and preserve medical and pharmacological expertise.

• PV staff must be involved in their development, validation, and implementation.

• Regulators need to move toward system interoperability for making reporting less time-consuming and avoid missing information.

• Speech recognition and text summarization tools will reduce the workload of drug information centers.

• Robotic process automation will automate reminders for follow-up information, and case transmission to authorities.

• Automated tools will be implemented in quality control and informativity of source’ cases.

• IA tools for signal detection, both via case-by-case analysis and via data mining techniques, seem not enough developed for short-term implementation.

• AI and IA tools will not replace human expertise, but their integration into routine PV activities will improve PV systems’ performance and, ultimately, safety issue identification.

Acknowledgments

Authors would like to express sincere gratitude to Pr Gayo Diallo for their invaluable assistance, inputs, and support throughout the finalization of this work.

Declaration of Interest

L Létinier was employed by Synapse Medicine at the time this research was conducted or held stock/stock options therein. 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 materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

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

Author contribution statement

All authors contributed to the conception and design of the study, data acquisition and analysis, and interpretation of the results. F Salvo drafted the manuscript, and all authors critically revised it for important intellectual content and approved the submitted version.

Data availability statement

Concerning data sharing, this is not applicable to the present article as no new data were created or analyzed in this study.

Additional information

Funding

This paper was not funded.