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Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance

TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance processes. MCs increased the number and extent of implementation of intelligent automation solutions throughout Individual Case Safety Report (ICSR) proc...

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Autores principales: Kassekert, Raymond, Grabowski, Neal, Lorenz, Denny, Schaffer, Claudia, Kempf, Dieter, Roy, Promit, Kjoersvik, Oeystein, Saldana, Griselda, ElShal, Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114066/
https://www.ncbi.nlm.nih.gov/pubmed/35579809
http://dx.doi.org/10.1007/s40264-022-01164-5
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author Kassekert, Raymond
Grabowski, Neal
Lorenz, Denny
Schaffer, Claudia
Kempf, Dieter
Roy, Promit
Kjoersvik, Oeystein
Saldana, Griselda
ElShal, Sarah
author_facet Kassekert, Raymond
Grabowski, Neal
Lorenz, Denny
Schaffer, Claudia
Kempf, Dieter
Roy, Promit
Kjoersvik, Oeystein
Saldana, Griselda
ElShal, Sarah
author_sort Kassekert, Raymond
collection PubMed
description TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance processes. MCs increased the number and extent of implementation of intelligent automation solutions throughout Individual Case Safety Report (ICSR) processing, especially with rule-based automations such as robotic process automation, lookups, and workflows, moving from planning to piloting to implementation over the 3 survey years. Companies remain highly interested in other technologies such as machine learning (ML) and artificial intelligence, which can deliver a human-like interpretation of data and decision making rather than just automating tasks. Intelligent automation solutions are usually used in combination with more than one technology being used simultaneously for the same ICSR process step. Challenges to implementing intelligent automation solutions include finding/having appropriate training data for ML models and the need for harmonized regulatory guidance.
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spelling pubmed-91140662022-05-19 Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance Kassekert, Raymond Grabowski, Neal Lorenz, Denny Schaffer, Claudia Kempf, Dieter Roy, Promit Kjoersvik, Oeystein Saldana, Griselda ElShal, Sarah Drug Saf Leading Article TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance processes. MCs increased the number and extent of implementation of intelligent automation solutions throughout Individual Case Safety Report (ICSR) processing, especially with rule-based automations such as robotic process automation, lookups, and workflows, moving from planning to piloting to implementation over the 3 survey years. Companies remain highly interested in other technologies such as machine learning (ML) and artificial intelligence, which can deliver a human-like interpretation of data and decision making rather than just automating tasks. Intelligent automation solutions are usually used in combination with more than one technology being used simultaneously for the same ICSR process step. Challenges to implementing intelligent automation solutions include finding/having appropriate training data for ML models and the need for harmonized regulatory guidance. Springer International Publishing 2022-05-17 2022 /pmc/articles/PMC9114066/ /pubmed/35579809 http://dx.doi.org/10.1007/s40264-022-01164-5 Text en © TransCelerate BioPharma Inc. 2022 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Leading Article
Kassekert, Raymond
Grabowski, Neal
Lorenz, Denny
Schaffer, Claudia
Kempf, Dieter
Roy, Promit
Kjoersvik, Oeystein
Saldana, Griselda
ElShal, Sarah
Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance
title Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance
title_full Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance
title_fullStr Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance
title_full_unstemmed Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance
title_short Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance
title_sort industry perspective on artificial intelligence/machine learning in pharmacovigilance
topic Leading Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9114066/
https://www.ncbi.nlm.nih.gov/pubmed/35579809
http://dx.doi.org/10.1007/s40264-022-01164-5
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