Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development.
The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review.
Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as ‘pharmacovigilance,’ ‘patient safety,’ ‘artificial intelligence,’ and ‘machine learning’ in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded.
Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug–drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source.
Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.
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No funding was received. All authors are members of the North America Chapter of the International Society of Pharmacovigilance (NASoP), which is a non-profit scientific organization.
Conflicts of interest
Maribel Salas and Priyanka Yalamanchili are employees of Daiichi Sankyo, Inc. Jan Petracek is the founder and director of the Institute of Pharmacovigilance. Omar Aimer is an employee of Innovigilance. Dinesh Kasthuril is an employee of Labcorp Drug Development. Sameer Dhingra is Associate Professor and Head of Department of Pharmacy Practice at National Institute of Pharmaceutical Education and Research (NIPER), Hajipur. Toluwalope Junaid is an employee of Syneos Health. Tina Bostic is an employee of PPD, part of Thermo Fisher Scientific. The opinions and positions taken in this article are personal to the authors and not their employer/affiliated institutions.
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MS: study design, study implementation (title/abstract screening, quality control), preparation of draft manuscript, review of manuscript draft, and interpretation of results. JP: study design, study implementation (title/abstract screening), review of manuscript draft, and interpretation of results. PY: study design, study implementation (full article screening, quality control), preparation of draft manuscript, review of manuscript draft, and interpretation of results. OA: study design, study implementation (title/abstract screening, full article screening), review of manuscript draft, and interpretation of results. DK: study design, study implementation (full article screening), review of manuscript draft, and interpretation of results. SD: review of manuscript draft and interpretation of results. TJ: study implementation (quality control), review of manuscript draft, and interpretation of results. TB: study design, study implementation (title/abstract screening, quality control), review of manuscript draft, interpretation of results, quality control of summary table. All authors have read and approved the final version of the manuscript and agree to be accountable for the work presented.
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Salas, M., Petracek, J., Yalamanchili, P. et al. The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature. Pharm Med 36, 295–306 (2022). https://doi.org/10.1007/s40290-022-00441-z