Abstract View

Author(s): Harshal G. Patil*1, Vinit S. Khairnar2

Email(s): 1harshpatil6006@gmail.com

Address:

    Ahinsa Institute of Pharmacy, Dhule road, Dondaicha, Tal. Shindkheda, Dist. Dhule, Maharashtra

Published In:   Volume - 4,      Issue - 5,     Year - 2025

DOI: https://doi.org/10.71431/IJRPAS.2025.4503  

 View HTML        View PDF

Please allow Pop-Up for this website to view PDF file.

ABSTRACT:
Artificial Intelligence plays a transformative role in Pharmacovigilance emphasizing its huge impact on detection of adverse drug reactions rather effectively. Various studies were analysed thoroughly identifying key AI applications such as Natural language processing and Machine learning alongside predictive modelling techniques very effectively. It highlights significant challenges such as complex data quality issues and gaps in regulatory frameworks alongside significant infrastructure shortcomings and big limitations. AI's transformative potential in Pharmacovigilance remains evident despite existing challenges and future tech policy collaboration will further bolster drug safety greatly.

Cite this article:
Harshal G. Patil, Vinit S. Khairnar. Impact of AI on Pharmacovigilance: A Systematic Review. IJRPAS, May 2025; 4 (5): 26-35.DOI: https://doi.org/https://doi.org/10.71431/IJRPAS.2025.4503


1)      Fu, L., Jia, G., Liu, Z., Pang, X., & Cui, Y. (2025). The applications and advances of artificial intelligence in drug regulation: A global perspective. Acta Pharmaceutica Sinica B, 15(1), 1–14. https://doi.org/10.1016/j.apsb.2024.11.006

2)      Bekbolatova, M., Mayer, J., Ong, C. W., & Toma, M. (2024). Transformative potential of AI in healthcare: Definitions, applications, and navigating the ethical landscape and public perspectives. Healthcare, 12(2), 125. https://doi.org/10.3390/healthcare12020125

3)      World Health Organization. Pharmacovigilance [Internet]. Geneva: WHO; 2024  https://www.who.int/teams/regulation-prequalification/pharmacovigilance

4)      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

5)      Ahire YS, Patil JH, Chordiya HN, Deore 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

6)      Al-Garadi MA, Yang YC, Sarker 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. doi: 10.3390/healthcare10112270. PMID: 36421593; PMCID: PMC9690240.

7)      Madan, S., Lentzen, M., Brandt, J. et al. Transformer models in biomedicine. BMC Med Inform Decis Mak 24, 214 (2024). https://doi.org/10.1186/s12911-024-02600-5

8)      Fu, L., Jia, G., Liu, Z., Pang, X., & Cui, Y. (2024). The applications and advances of artificial intelligence in drug regulation: A global perspective. Acta Pharmaceutica Sinica B, 15(1), 1–14. https://doi.org/10.1016/j.apsb.2024.11.006

9)      Kawamura, K., et al. (2024). Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Deep Learning Model Evaluation. Journal of Medical Internet Research, 26, e55794. https://doi.org/10.2196/55794Murali K, Kaur S, Prakash A, Medhi B. Artificial intelligence in pharmacovigilance: Practical utility. Indian J Pharmacol. 2019 Nov-Dec;51(6):373-376. Doi: 10.4103/ijp.IJP_814_19. Epub 2020 Jan 16. PMID: 32029958; PMCID: PMC6984023.

10)  Rekha, B.H., Hisham, S.A., Wahab, I.A. et al. Digital monitoring of medication safety in children: an investigation of ADR signalling techniques in Malaysia. BMC Med Inform Decis Mak 24, 395 (2024). https://doi.org/10.1186/s12911-024-02801-y

11)  DIA Global Forum. (2024). A new pharmacovigilance ecosystem: Automation, AI, and continuous improvement. DIA Global Forum. https://globalforum.diaglobal.org/issue/september-2024/a-new-pharmacovigilance-ecosystem-automation-ai-and-continuous-improvement/?utm_source=chatgpt.com

12)  Springer Nature. (2025). Artificial intelligence: Applications in pharmacovigilance signal detection. Nature Reviews Drug Discovery, 24(2), 123–135. https://doi.org/10.1007/s40290-025-00561-2?utm_source=chatgpt.com

13)  IQVIA. (2024). Enhancing pharmacovigilance intake processes with AI and automation. IQVIA Blogs. https://www.iqvia.com/blogs/2024/12/enhancing-pharmacovigilance-intake-processes-with-ai-and-automation?utm_source=chatgpt.com

14)  Ferreira, R. D. A., Zhong, S., Moureaud, C., Le, M. T., Rothstein, A., Li, X., Wang, L., & Patwardhan, M. (2024). A pilot, predictive surveillance model in pharmacovigilance using machine learning approaches. Advances in Therapy, 41(6), 2435–2445. https://doi.org/10.1007/s12325-024-02870-5

15)  Painter JL, Kassekert R, Bate A. An industry perspective on the use of machine learning in drug and vaccine safety. Front Drug Saf Regul. 2023 Feb 1;3:1110498. doi: 10.3389/fdsfr.2023.1110498.

16)  Seal, S., Williams, D., Hosseini-Gerami, L., Mahale, M., Carpenter, A. E., Spjuth, O., & Bender, A. (2024). Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data. Chemical Research in Toxicology, 37(8), 1290–1305. https://doi.org/10.1021/acs.chemrestox.4c00015

17)  Seal, S., Spjuth, O., Hosseini-Gerami, L., García-Ortegón, M., Singh, S., Bender, A., & Carpenter, A. E. (2024). Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank. Journal of Chemical Information and Modeling, 64(4), 293–308. https://doi.org/10.1021/acs.jcim.3c01834

18)  Desai MK. Artificial intelligence in pharmacovigilance - Opportunities and challenges. Perspect Clin Res. 2024 Jul-Sep;15(3):116-121. doi: 10.4103/picr.picr_290_23. Epub 2024 Mar 27. PMID: 39140015; PMCID: PMC11318788.

19)  Bate, A., & Stegmann, J.-U. (2023). Artificial intelligence and pharmacovigilance: What is happening, what could happen and what should happen? Health Policy and Technology, 12(2), 100743. https://doi.org/10.1016/j.hlpt.2023.100743

20)  Liang, L., Hu, J., Sun, G., Hong, N., Wu, G., He, Y., Li, Y., Hao, T., Liu, L., & Gong, M. (2022). Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources. Drug Safety, 45(5), 511-519. https://doi.org/10.1007/s40264-022-01170-7

21)  Nikfarjam, A., Sarker, A., O'Connor, K., Ginn, R., & Gonzalez, G. (2015). Pharmacovigilance from social media: Mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association: JAMIA, 22(3), 671–681. https://doi.org/10.1093/jamia/ocu041

Related Images:



Recent Images



Quality by Design Based Analytical Method Development and Validation of Sitagliptin Phosphate by RP-HPLC
An Appraisal On Diclofenac and Its Undesirable Effects: From Pain Relief to Systemic Damage
Novel Approach In Treatment of Cholangiocarcinoma: A Review
High Performance Thin Layer Chromatography method for estimation of Rosuvastatin Calcium and Teneligliptin Hydrobromide Hydrate from its tablet dosage form
Formulation, Development and Evaluation of NDDS Formulation for Treatment of Parkinson’s Disease
A Systematic Review of Cyperus rotundus Linn as an Massive Source of Pharmacologically Active Phyto-Medicine
Miraculous Moringa: A Comprehensive Explanation of its Botanical, Nutritional and Medicinal Potentials
Phytochemical Screening, Formulation and Evaluation of Leaf Extract of Leucas aspera and Leucas indica by Using Analytical Methods
RPHPLC Method for Concurrent Determination of Haloperidol and Trihexyphenidyl in API and Combined Tablet Formulations
A review on HPLC Methods for Estimation of Travoprost  in Combined and Single Pharmaceutical formulation and Bulk

Tags


Recomonded Articles:

Author(s): Devadatta Pandurang Hatim; Sachinkumar V. Patil; Sachin Mali

DOI:         Access: Open Access Read More

Author(s): S. Sathya, Karthiga. D, Lokesh. S, Sabari Manikandan, V. R. Rajeswari

DOI: https://doi.org/10.71431/IJRPAS.2025.4105         Access: Open Access Read More

Author(s): Quazi Kamil Hafiz Anees Ahemad; Dr. Majaz Quazi; Quazi Wasil; Dr. G. J. Khan

DOI:         Access: Open Access Read More

Author(s): Nilesh R. Suryawanshi; Karan. A. Patil; Nikhil. J. Rajput; H. P. Suryawanshi; R. A. Ahirrao; J. I. Pinjari

DOI:         Access: Open Access Read More

Author(s): Harshal G. Patil*, Vinit S. Khairnar

DOI: https://doi.org/10.71431/IJRPAS.2025.4503         Access: Open Access Read More

Author(s): Dnyaneshwari A. Gunjal*; Chaitali R. Rajput; Vaishnavi P. Wani; Madhuri S. Pardeshi

DOI: https://doi.org/10.71431/IJRPAS.2025.4201         Access: Open Access Read More

Author(s): Fadilullahi Opeyemi Ibiyemi*1, Ismail Kolawole Odetayo2, Fareedah Adeshina3

DOI: https://doi.org/10.71431/IJRPAS.2025.4707         Access: Open Access Read More

Author(s): Poduri Lakshmi Lohita Priya*; Syed Tehameem Afzal; Sheik Arshiya Anjum

DOI: https://doi.org/10.71431/IJRPAS.2025.4702         Access: Open Access Read More