Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1784709703165542400 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9114066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kassekertraymond industryperspectiveonartificialintelligencemachinelearninginpharmacovigilance AT grabowskineal industryperspectiveonartificialintelligencemachinelearninginpharmacovigilance AT lorenzdenny industryperspectiveonartificialintelligencemachinelearninginpharmacovigilance AT schafferclaudia industryperspectiveonartificialintelligencemachinelearninginpharmacovigilance AT kempfdieter industryperspectiveonartificialintelligencemachinelearninginpharmacovigilance AT roypromit industryperspectiveonartificialintelligencemachinelearninginpharmacovigilance AT kjoersvikoeystein industryperspectiveonartificialintelligencemachinelearninginpharmacovigilance AT saldanagriselda industryperspectiveonartificialintelligencemachinelearninginpharmacovigilance AT elshalsarah industryperspectiveonartificialintelligencemachinelearninginpharmacovigilance |