Front. Drug Saf. Regul. Frontiers in Drug Safety and Regulation Front. Drug Saf. Regul. 2674-0869 Frontiers Media S.A. 1244115 10.3389/fdsfr.2023.1244115 Drug Safety and Regulation Correction Corrigendum: An industry perspective on the use of machine learning in drug and vaccine safety Painter et al. 10.3389/fdsfr.2023.1244115 Painter Jeffery L. 1 Kassekert Raymond 2 Bate Andrew 3 4 * 1 GlaxoSmithKline, Global Safety, Durham, NC, United States 2 GlaxoSmithKline, Global Safety, Upper Providence, PA, United States 3 GlaxoSmithKline, Global Safety, Brentford, Middlesex, United Kingdom 4 London School of Hygiene and Tropical Medicine, London, United Kingdom

Edited by: Taxiarchis Botsis, Johns Hopkins University, United States

Reviewed by: Cristiano Matos, Escola Superior de Tecnologia da Saúde de Coimbra, ESTeSC-IPC, Portugal

 *Correspondence: Andrew Bate, andrew.x.bate@gsk.com 04 12 2023 2023 3 1244115 21 06 2023 30 06 2023 Copyright © 2023 Painter, Kassekert and Bate. 2023 Painter, Kassekert and Bate

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 A Corrigendum on An industry perspective on the use of machine learning in drug and vaccine safety by Painter JL, Kassekert R and Bate A (2023). Front. Drug. Saf. Regul. 3:1110498. doi: 10.3389/fdsfr.2023.1110498 pharmacovigilance machine learning-ML drug safety vaccines safety artificial intelligence section-at-acceptance Advanced Methods in Pharmacovigilance and Pharmacoepidemiology

In the published article, there was an error in Figure 1 as published. The figure failed to include the details on the individual node references. The corrected Figure 1 and its caption appear below.

The benefits of robotic process automation (RPA). Additional details are found in Table 1.

In the published article, Table 1 was mistakenly not included in the publication. The missing material appears below.

Details on the benefits of robotic process automation (RPA).

Node Phase Benefit Description
Expected Benefits
A Before RPA Enforcement of organizational policies Automation defaults to observing organizational policies in a systematic way
B Introduction of RPA Improvement in time efficiency Reduction in time for case processing and booking
D Introduction of RPA Reduction of manual tasks and workload Reduction of repetitive, mundane, tedious manual tasks
E Introduction of RPA Improvement in data accuracy Automate data processing in systematic way. Decrease potential errors and mistakes in safety case review and processing
F RPA in Systematic Use Decrease in compliance risk Timelier fulfillment of compliance and audit requirements for regulatory bodies
G RPA in Systematic Use Improvement in human resource utilization Better utilization of staff, focus on cases which require human intervention
J RPA in Systematic Use Return on investment Increase capability for high volume case processing without increasing staff
K RPA in Systematic Use Increase in operational reliability Allows the organization to operate reliably, even when faced with unexpected challenges
Unexpected Benefits
C Introduction of RPA Improved employee participation Staff is relieved from repetitive tasks and able to focus on improving overall business processes and decision making
H RPA in Systematic Use Improvement in transparency, visibility and better understanding of processes Business processes and business rules are judiciously executed, processes are documented and explainable to stakeholders
I RPA in Systematic Use Improvement in job satisfaction Empower staff to focus on the most important tasks which lead to better job fulfillment

In the published article, Table 1 should now be updated as Table 2 and any associated references to Table 1 should refer to Table 2, with no changes to its legend or content.

The authors apologize for this errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

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