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Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources
With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner, data-driven automatic methods need to be urgently applied to all aspects of pharmacovigilance to assist healthcare professionals. However, t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9112260/ https://www.ncbi.nlm.nih.gov/pubmed/35579814 http://dx.doi.org/10.1007/s40264-022-01170-7 |
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author | Liang, Likeng Hu, Jifa Sun, Gang Hong, Na Wu, Ge He, Yuejun Li, Yong Hao, Tianyong Liu, Li Gong, Mengchun |
author_facet | Liang, Likeng Hu, Jifa Sun, Gang Hong, Na Wu, Ge He, Yuejun Li, Yong Hao, Tianyong Liu, Li Gong, Mengchun |
author_sort | Liang, Likeng |
collection | PubMed |
description | With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner, data-driven automatic methods need to be urgently applied to all aspects of pharmacovigilance to assist healthcare professionals. However, the quantity and quality of data directly affect the performance of AI, and there are particular challenges to implementing AI in limited-resource settings. Analyzing challenges and solutions for AI-based pharmacovigilance in resource-limited settings can improve pharmacovigilance frameworks and capabilities in these settings. In this review, we summarize the challenges into four categories: establishing a database for an AI-based pharmacovigilance system, lack of human resources, weak AI technology and insufficient government support. This study also discusses possible solutions and future perspectives on AI-based pharmacovigilance in resource-limited settings. |
format | Online Article Text |
id | pubmed-9112260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-91122602022-05-17 Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources Liang, Likeng Hu, Jifa Sun, Gang Hong, Na Wu, Ge He, Yuejun Li, Yong Hao, Tianyong Liu, Li Gong, Mengchun Drug Saf Review Article With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner, data-driven automatic methods need to be urgently applied to all aspects of pharmacovigilance to assist healthcare professionals. However, the quantity and quality of data directly affect the performance of AI, and there are particular challenges to implementing AI in limited-resource settings. Analyzing challenges and solutions for AI-based pharmacovigilance in resource-limited settings can improve pharmacovigilance frameworks and capabilities in these settings. In this review, we summarize the challenges into four categories: establishing a database for an AI-based pharmacovigilance system, lack of human resources, weak AI technology and insufficient government support. This study also discusses possible solutions and future perspectives on AI-based pharmacovigilance in resource-limited settings. Springer International Publishing 2022-05-17 2022 /pmc/articles/PMC9112260/ /pubmed/35579814 http://dx.doi.org/10.1007/s40264-022-01170-7 Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Review Article Liang, Likeng Hu, Jifa Sun, Gang Hong, Na Wu, Ge He, Yuejun Li, Yong Hao, Tianyong Liu, Li Gong, Mengchun Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources |
title | Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources |
title_full | Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources |
title_fullStr | Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources |
title_full_unstemmed | Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources |
title_short | Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources |
title_sort | artificial intelligence-based pharmacovigilance in the setting of limited resources |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9112260/ https://www.ncbi.nlm.nih.gov/pubmed/35579814 http://dx.doi.org/10.1007/s40264-022-01170-7 |
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