Role of artificial intelligence in pharmacovigillance
Abstract
Pharmacovigilance (PV) is a data-driven process to identify medicine safety issues at the earliest by processing suspected adverse event (AE) reports and extraction of health data. The PV case processing cycle starts with data collection, data entry, initial checking completeness and validity, coding, medical assessment for causality, expectedness, severity, and seriousness, subsequently submitting report, quality checking followed by data storage and maintenance. This requires a workforce and technical expertise and therefore, is expensive and time- consuming. There has been exponential growth in the number of suspected AE reports in the PV database due to smart collection and reporting of individual case safety reports, widening the base by increased awareness and participation by health-care professionals and patients. Theprimary goal of pharmacovigilance, the cornerstone of public health, is to track and evaluate adverse drug reactions in order to guarantee patient safety. Conventional approaches suffer from biases in human error, inefficiency, and scalability problems. A new era in pharmacovigilance is being ushered in by the introduction of artificial intelligence (AI), which holds the promise of vast data analysis, automated procedures, and enhanced safety signal detection
Keywords:
Artificial intelligence, individual case safety reports processing, pharmacovigilance
References
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https://doi.org/10.1007/978-981-99-8949-2_17
2. Belton KJ, European Pharmacovigilance Research Group. Attitude survey of adverse drug-reaction reporting by health care professionals across the European Union. European journal of clinical pharmacology. 1997 Sep;52:423-7.
https://doi.org/10.1007/s002280050314
3. Bidollahkhani M, Kunkel JM. Revolutionizing system reliability: The role of AI in predictive maintenance strategies. arXiv preprint arXiv:2404.13454. 2024 Apr 20.
https://doi.org/10.1007/978-981-99-8949-2_17
4. Ball R, Dal Pan G. “Artificial intelligence” for pharmacovigilance: ready for prime time?. Drug safety. 2022 May;45(5):429-38.
https://doi.org/10.1007/s40264-022-01157-4
5. Khan MA, Hamid S, Babar ZU. Pharmacovigilance in high-income countries: current developments and a review of literature. Pharmacy. 2023 Jan 6;11(1):10.
https://doi.org/10.3390/pharmacy11010010
6. Lu M, Yin J, Zhu Q, Lin G, Mou M, Liu F, Pan Z, You N, Lian X, Li F, Zhang H. Artificial intelligence in pharmaceutical sciences. Engineering. 2023 Aug 1;27:37-69.
https://doi.org/10.1016/j.eng.2023.01.014
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https://doi.org/ 10.18579/jopcr/v23.1.24
8. Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S, Junaid T, Bostic T. The use of artificial intelligence in pharmacovigilance: a systematic review of the literature. Pharmaceutical medicine. 2022 Oct;36(5):295-306.
https://doi.org/10.1007/s40290-022-00441-z
9. Agbabiaka TB, Savović J, Ernst E. Methods for causality assessment of adverse drug reactions: a systematic review. Drug safety. 2008 Jan;31:21-37.
https://doi.org/10.2165/00002018-200831010-00003
10. Ibrahim H, Abdo A, El Kerdawy AM, Eldin AS. Signal detection in pharmacovigilance: a review of informatics-driven approaches for the discovery of drug-drug interaction signals in different data sources. Artificial intelligence in the life sciences. 2021 Dec 1;1:100005.
https://doi.org/10.1016/j.ailsci.2021.100005
11. Messeri L, Crockett MJ. Artificial intelligence and illusions of understanding in scientific research. Nature. 2024 Mar 7;627(8002):49-58.
https://doi.org/10.1038/s41586-024-07146-0
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https://doi.org/10.1073/pnas.1919012117
13. Thakur H, Chavhan S, Jotkar R, Mukherjee K. Developing clinical indicators for the secondary health system in India. International Journal for Quality in Health Care. 2008 Aug 1;20(4):297-303. https://doi.org/10.1093/intqhc/mzn012
14. Messeri L, Crockett MJ. Artificial intelligence and illusions of understanding in scientific research. Nature. 2024 Mar 7;627(8002):49-58.
https://doi.org/10.1038/s41586-024-07146-0
15. Ibrahim H, Abdo A, El Kerdawy AM, Eldin AS. Signal detection in pharmacovigilance: a review of informatics-driven approaches for the discovery of drug-drug interaction signals in different data sources. Artificial intelligence in the life sciences. 2021 Dec 1;1:100005. https://doi.org/10.1016/j.ailsci.2021.100005
16. Al-Worafi YM. Artificial intelligence and machine learning for drug safety. InTechnology for drug safety: Current status and future developments 2023 Jul 19 (pp. 69-80). Cham: Springer International Publishing.
https://doi.org/10.1007/978-3-031-34268-4_7
17. Edrees H, Song W, Syrowatka A, Simona A, Amato MG, Bates DW. Intelligent telehealth in pharmacovigilance: a future perspective. Drug safety. 2022 May;45(5):449-58 https://doi.org/10.1007/s40264-022-01172-5
18. Hauben M. Artificial intelligence and data mining for the pharmacovigilance of drug– drug interactions. Clinical Therapeutics. 2023 Feb 1;45(2):117-33. https://doi.org/10.1016/j.clinthera.2023.01.002
19. Ball R, Dal Pan G. “Artificial intelligence” for pharmacovigilance: ready for prime time?. Drug safety. 2022 May;45(5):429-38.
https://doi.org/10.1007/s40264-022-01157-4
20. Bellamy D, Celi L, Beam AL. Evaluating progress on machine learning for longitudinal electronic healthcare data. arXiv preprint arXiv:2010.01149. 2020 Oct 2. https://doi.org/10.48550/arXiv.2010.01149
21. LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE. 1998 Nov;86(11):2278-324.
10.1109/5.726791
22. Salvo F, Micallef J, Lahouegue A, Chouchana L, Létinier L, Faillie JL, Pariente A. Will the future of pharmacovigilance be more automated?. Expert Opinion on Drug Safety. 2023 Jul 3;22(7):541-8. https://doi.org/10.1080/14740338.2023.2227091
23. Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S, Junaid T, Bostic T. The use of artificial intelligence in pharmacovigilance: a systematic review of the literature. Pharmaceutical medicine. 2022 Oct;36(5):295-306.
https://doi.org/10.1007/s40290-022-00441-z
24. Kalaiselvan V, Sharma A, Gupta SK. “Feasibility test and application of AI in healthcare”—with special emphasis in clinical, pharmacovigilance, and regulatory practices. Health and Technology. 2021 Jan;11:1-5.
https://doi.org/10.1007/s12553-020-00495-6
25. Lamberti MJ, Wilkinson M, Donzanti BA, Wohlhieter GE, Parikh S, Wilkins RG, Getz K. A study on the application and use of artificial intelligence to support drug development. Clinical therapeutics. 2019 Aug 1;41(8):1414-26.
https://doi.org/10.1016/j.clinthera.2019.05.018
26. Askin S, Burkhalter D, Calado G, El Dakrouni S. Artificial intelligence applied to clinical trials: opportunities and challenges. Health and technology. 2023 Mar;13(2):203-13. https://doi.org/10.1007/s12553-023-00738-2
27. Hwisa NT, Gindi S, Rao CB, Katakam P, Rao Chandu B. Evaluation of Antiulcer Activity of Picrasma Quassioides Bennett Aqueous Extract in Rodents. Vedic Res. Int. Phytomedicine. 2013;1:27.
https://www.researchgate.net/publication/248995990
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Talluri, P. (2025). Role of artificial intelligence in pharmacovigillance. Journal of Integral Sciences, 8(3), 39-45. Retrieved from https://jisciences.com/index.php/journal/article/view/117
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