Volume : 12, Issue : 09, September – 2025

Title:

THE TRANSFERMATIVE IMPACT OF ARTIFICIAL INTELLEGENCE IN PHARMACEUTICAL INDUSTRY – AN OVERVIEW

Authors :

Dr. P.Veera Lakshmi, Jethendra Lakshman Sai.S* , Bhavya Tejaswi.G, Aruna Kumari.K , Surya Kiran .M

Abstract :

This overview highlights the transformative impact of AI in the pharmaceutical industry, providing insights into current applications, technological advancements, and future directions aimed at reshaping drug discovery, development, and patient care. While addressing the need for ethical and regulatory frameworks to promote equitable and safe AI adoption, the current analysis focuses on the revolutionary potential of AI and examines its many important applications, prospects, and challenges in the pharmaceutical business. This paper provides information regarding artificial intelligence (AI), which plays a transformative role in various departments of the pharmaceutical industry both now and in the future.

Cite This Article:

Please cite this article in press Jethendra Lakshman sai.S et al., The Transfermative Impact Of Artificial Intellegence In Pharmaceutical Industry – An Overview, Indo Am. J. P. Sci, 2025; 12(09).

REFERENCES:

1. Sampene, A. K., & Nyirenda, F. (2024b). Evaluating the effect of artificial intelligence on pharmaceutical product and drug discovery in China. Future Journal of Pharmaceutical Sciences, 10(1). https://doi.org/10.1186/s43094-024-00632-2
2. Saini, J. P. S., Thakur, A., & Yadav, D. (2025b). AI driven Innovations in Pharmaceuticals: Optimizing Drug Discovery and Industry Operations. RSC Pharmaceutics. https://doi.org/10.1039/d4pm00323c
3. Sci, C. S. F. P. (2018). Conference 2018: Translating Innovative Technology to Patient Care. An international symposium held jointly by CSPS, CSPT, and CC-CRS, May 22-25, 2018, Toronto, ON, Canada. Journal of Pharmacy & Pharmaceutical Sciences, 21, 1s–162s. https://doi.org/10.18433/jpps30085
4. Prashant Singh, Asheesh Kumar Singh, Navneet Kumar Verma, Abhay Kumar, Zahra Chegini, Ankita Malviya. The Transformative Role of Artificial Intelligence in Pharmaceutical Healthcare: A Comprehensive Review. Sch Acad J Pharm, 2024 May 13(5): 139-144.
5. Salehi, F. (2024). The Transformative Role of Artificial Intelligence in the Healthcare Industry: A Comprehensive Analysis. Asian Journal of Research in Medicine and Medical Science, 6(1), 62–70.
6. Oualikene-Gonin W, Jaulent MC, Thierry JP, Oliveira-Martins S, Belgodère L, Maison P, Ankri J; Scientific Advisory Board of ANSM. Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities. Front Pharmacol. 2024 Aug 2;15:1437167. doi: 10.3389/fphar.2024.1437167. PMID: 39156111; PMCID: PMC11327028.
7. Balisa Mosisa Ejeta, Malay K Das, Sanjoy Das, Fetene Fufa Bekere, Dubom Tayeng (2024). “Transformative Role of Artificial Intelligence in the Pharmaceutical Sector”, Journal of Angiotherapy, 8(9),1-7,9933
8. Kolluri, S., Lin, J., Liu, R. et al. Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review. AAPS J 24, 19 (2022). https://doi.org/10.1208/s12248-021-00644-3
9. The Transformative potential of artificialintelligence, Futures,Volume135,2022,102884, ISSN00163287,https://doi.org/10.1016/j.futures.2021.10288(https://www.sciencedirect.com/science/article/pii/S0016328721001932).
10. Wu G, Yang F. Navigating the Transformative Impact of Artificial Intelligence in Health Services Research. Health Sci Rep. 2025 Jun 4;8(6):e70793. doi: 10.1002/hsr2.70793. PMID: 40475792; PMCID: PMC12138046.
11. Chopra H, Annu, Shin DK, Munjal K, Priyanka, Dhama K, Emran TB. Revolutionizing clinical trials: the role of AI in accelerating medical breakthroughs. Int J Surg. 2023 Dec 1;109(12):4211-4220. doi: 10.1097/JS9.0000000000000705. PMID: 38259001; PMCID: PMC10720846.
12. Askin S, Burkhalter D, Calado G, et al. Artificial Intelligence Applied to clinical trials: opportunities and challenges. Health Technol (Berl) 2023;13:203–213.
13. Saama Launches Industry’s First AI-driven Data Platform to Accelerate Clinical Development | Saama – #1 in AI Clinical Analytics. Accessed 3 July 2023.
14. C. Malamateniou, S. McFadden, Y. McQuinlan, A. England, N. Woznitza, S. Goldsworthy, C. Currie, E. Skelton, K.-Y. Chu, N. Alware, P. Matthews, R. Hawkesford, R. Tucker, W. Town, J. Matthew, C. Kalinka, T. O’Regan,Artificial Intelligence: Guidance for clinical imaging and therapeutic radiography professionals, a summary by the Society of Radiographers AI working group,Radiography,Volume 27, Issue 2021,Pages 1192 1202,ISSN 1078-8174,https://doi.org/10.1016/j.radi.2021.07.028.
15. Jaskaran Preet Singh Saini , Ankita Thakur , Deepak Yadav -AI-driven innovations in pharmaceuticals: optimizing drug discovery and industry operations , 2025, 2, 437-454,DOI: 10.1039/D4PM00323C
16. Ubels J, Schaefers T, Punt C, Guchelaar HJ, de Ridder J. RAINFOREST: a random forest approach to predict treatment benefit in data from (failed) clinical drug trials. Bioinformatics.2020Dec 30;36(Suppl_2):i601-i609. doi: 10.1093/bioinformatics/btaa799. PMID: 33381829.
17. Ebube NK, Owusu-Ababio G, Adeyeye CM. Preformulation studies and characterization of the physicochemical properties of amorphous polymers using artificial neural networks. Int J Pharm. 2000 Feb 25;196(1):27-35. doi: 10.1016/s0378-5173(99)00405-6. PMID: 10675705.
18. Liu B, He H, Luo H, Zhang T, Jiang J. Artificial intelligence and big data facilitated targeted drug discovery. Stroke Vasc Neurol. 2019 Nov 7;4(4):206-213. doi: 10.1136/svn-2019-000290. PMID: 32030204; PMCID: PMC6979871.
19. Cirillo D, Valencia A. Big data analytics for personalized medicine. Curr Opin Biotechnol. 2019 Aug;58:161-167. doi: 10.1016/j.copbio.2019.03.004. Epub 2019 Apr 6. PMID: 30965188.
20. https://www.iqvia.com/blogs/2024/11/the-crucial-role-of-real-time-data-use-in-pharma-marketing
21. Miranda Schmalfuhs-Personalized AI-Powered Solutions for Better Patient Adherence ,December 4, 2024
22. De Abreu Ferreira R, Zhong S, Moureaud C, Le MT, Rothstein A, Li X, Wang L, Patwardhan M. A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches. Adv Ther. 2024 Jun;41(6):2435-2445. doi: 10.1007/s12325-024-02870-5. Epub 2024 May 5. PMID: 38704799; PMCID: PMC11133112.
23. Gosselt HR, Bazelmans EA, Lieber T, van Hunsel FPAM, Härmark L. Development of a multivariate prediction model to identify individual case safety reports which require clinical review. Pharmacoepidemiol Drug Saf. 2022;31(12):1300–1307. doi: 10.1002/pds.5553.