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Author(s): Dhanashree Ajay Somavanshi1, * Dhanashri Kiran Gosawi2, Devesh Pravinkumar Bhavsar3

Email(s): 1dhanashreesomavanshi66@gmail.com

Address:

    Khandesh Education Society's Late Shri. Pandharinath Chhagansheth Bhandarkar College of D. Pharmacy & Late Prof R. K. Kele College of B. Pharmacy, Amalner- 425401 Dist-Jalgaon (M.S.) India

Published In:   Volume - 4,      Issue - 12,     Year - 2025

DOI: https://doi.org/10.71431/IJRPAS.2025.41207  

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ABSTRACT:
This review systematically explore the evolving role of artificial intelligence ,in pharmacovigilance, synthesizing current application that harness its potential for efficiency and insights, while critically examine the key challenges that requires careful governance to navigate the associated risk. Pharmacovigilance (PV) system is crucial for insuring drug safety, but they face increasing pressure from the exponential growth of complex, diverse data sources (ex. Electronic health records, spontaneous repots and social media). Artificial intelligence (AI) has become a game changer in pharmacovigilance (PV). AI methods, including natural language processing (NLP), machine learning (ML), and deep learning (DL), allow for automated case handling, signal detection, adverse drug reaction (ADR) forecasting, and risk management. This review discusses how AI has evolved, its applications, benefits, and limitations in pharmacovigilance, as well as future directions for promoting safe and effective medication use.

Cite this article:
Dhanashree Ajay Somavanshi, Dhanashri Kiran Gosawi, Devesh Pravinkumar Bhavsar. Review on Artificial Intelligence in Pharmacovigilance. IJRPAS, December 2025; 4(12): 74-80.DOI: https://doi.org/https://doi.org/10.71431/IJRPAS.2025.41207


  1. Arlett, P., et al. "Artificial Intelligence in Post-Marketing Drug Safety Surveillance." Drug Safety, 2022. 
  2. Sakaeda, T., et al. "Adverse Event Detection Using Machine Learning Approaches." Pharmacology Research & Perspectives, 2021. 
  3. Harpaz, R., et al. "Advanced Computational Approaches for Pharmacovigilance." Nature Reviews Drug Discovery, 2020.
  4. FDA. “Use of Artificial Intelligence in Drug Safety Monitoring.” U.S. Food and Drug Administration, 2023. 
  5. EMA. “Guidance on AI in Pharmacovigilance.” European Medicines Agency, 2022.
  6. Barr A, Feigenbaum E, Roads C. The Handbook of Artificial Intelligence, Volume 1. Comput Music J. 1982; 1:3–14. [Google Scholar]
  7. Mockute R, Desai S, Perera S, Assuncao B, Danysz K, Tetarenko N, et al. Artificial Intelligence within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation. Pharmaceut Med. 2019; 33:109–20. Doi: 10.1007/s40290-019-00269-0. [DOI] [PubMed] [Google Scholar].
  8. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018; 2:719–31. D oi: 10.1038/s41551-018-0305-z. [DOI] [PubMed] [Google Scholar
  9. World Health Organization. Pharmacovigilance [Internet]. Geneva: WHO; 2024  https://www.who.int/teams/regulation-prequalification/pharmacovigilance U.S. Food and Drug Administration. FDA Adverse Event Reporting System (FAERS) [Internet]. Silver Spring (MD): FDA; 2024 Dec 5  https://www.fda.gov/drugs/surveillance/fdas-adverse-event-reporting-system-faers
  10. Ahire YS, Patil JH, Chordiya HN, Deere RA, Bairagi VA. Advanced applications of artificial intelligence in pharmacovigilance: Current trends and future perspectives. J Pharm Res. 2024; 23(1):23–33.  https://jopcr.com/articles/advanced-applications-of-artificial-intelligence-in-pharmacovigilance-current-trends-and-future-perspectives
  11. Al-Garadi MA, Yang YC, Sarkar A. The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. Healthcare (Basel). 2022 Nov 12; 10(11):2270. Doe: 10.3390/healthcare10112270. PMID: 36421593; PMCID: PMC9690240.
  12. Abatemarco D, Perera S, Boa SH, Desai S, Asuncion B, Tetarenko N, et al. Training augmented intelligent capabilities for pharmacovigilance: applying deep-learning approaches to individual case safety report processing. Pharmacist Med. 2018; 32(6):391–401. 10.
  13. Alvaro N, Conway M, Doan S, Loft C, Overington J, Collier N. Crowdsourcing Twitter annotations to identify frst-hand experiences of prescription drug use. J Biomed Inform. 2015;58:280–7

 

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