Artificial Intelligence in Pharmaceutical Regulatory Affairs and Medical Science Liaison Activities: A Comprehensive Review

  • Sayashree Ananthan Sri Ramakrishna Institute of Paramedical Sciences College of Pharmacy, Sarojini Naidu Rd, Siddhapudur, Coimbatore, Tamil Nadu 641044

Abstract

Artificial Intelligence (AI) is transforming pharmaceutical regulatory affairs and Medical Science Liaison (MSL) activities by improving efficiency, accuracy, and decision-making. In regulatory affairs, AI enables automation in dossier preparation, regulatory intelligence, and pharmacovigilance, significantly reducing human error and turnaround times. For MSLs, AI facilitates analysis of large datasets, identification of Key Opinion Leaders (KOLs), and personalized communication with healthcare professionals. Despite these advancements, challenges such as data privacy, algorithmic transparency, and bias in AI algorithms persist. This review synthesizes recent studies and provides insights into the applications, benefits, challenges, and future directions of AI in these critical domains of pharmaceutical operations.

Keywords: Artificial Intelligence, Regulatory Affairs, Medical Science Liaison, Pharmacovigilance, Regulatory Intelligence, Real-World Evidence, eCTD, Quality by Design (QbD), Machine Learning, Healthcare Data Science

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References

1. Ventola CL. Big data and artificial intelligence in healthcare: opportunities and challenges. PT. 2019;44(7):387–91.
2. Makary MA, Daniel M. Artificial intelligence in regulatory submissions: transforming the future of pharmaceutical approvals. Regul Toxicol Pharmacol. 2020;113:104624.
3. Reddy S, Fox J, Purohit MP. Artificial intelligence–enabled regulatory intelligence: applications and challenges. Digit Health. 2019;5:1–12.
4. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94–8.
5. Sarker A, Klein A, Gonzalez G. Portable automatic text classification for adverse drug reaction detection. J Biomed Inform. 2019;90:103102.
6. Patel V, Fang H. Role of artificial intelligence in medical affairs: transforming communication and scientific engagement. Ther Innov Regul Sci. 2020;54(6):1353–60.
7. Kayaalp M. Evaluation of natural language processing systems in medical science liaison activities. Artif Intell Med. 2021;114:102051.
8. Yu KH, Kohane IS. Framing the challenges of artificial intelligence in healthcare. JAMA. 2019;322(17):1637–8.
9. Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310.
10. Coorey G, Figtree GA, Ainsworth R, Morris J. Artificial intelligence for benefit–risk assessment in regulatory science. Clin Pharmacol Ther. 2021;109(1):48–53.
11. Nasr SH, Maher M, Abou-Ghalia A. Artificial intelligence in Quality by Design: applications in pharmaceutical manufacturing. Pharm Dev Technol. 2022;27(2):103–12.
12. US Food and Drug Administration. Artificial intelligence and machine learning for electronic regulatory submissions: industry perspectives. FDA White Paper. 2023;1–18.
13. Ghosh R, Lee J. AI-powered virtual medical scientific support: a new era of medical information services. Med Aff J. 2022;7(3):112–9.
14. Bedenbender S, Morrison R. Predictive analytics for medical science liaison engagement. J Med Mark. 2020;20(2):65–73.
15. Sherman RE, Davies M. Real-world evidence generation using artificial intelligence: implications for medical affairs. Clin Ther. 2021;43(2):251–60.
16. Woodcock J, Woosley R. US FDA. The FDA critical path initiative and its influence on regulatory science [Internet]. Bethesda (MD): US FDA; 2019 [cited 2025 Jan 10]. Available from: https://www.fda.gov
17. Eichler HG, Abadie E, Baker M, Rasi G. Bridging adaptive pathways and real-world evidence [Internet]. Hoboken (NJ): Wiley; 2020 [cited 2025 Jan 10]. Available from: https://ascpt.onlinelibrary.wiley.com
18. Getz KA. Transforming medical affairs through data science and analytics [Internet]. Thousand Oaks (CA): SAGE Publications; 2021 [cited 2025 Jan 10]. Available from: https://journals.sagepub.com
19. Kotsiantis SB, Zaharakis ID, Pintelas PE. Machine learning: a review of classification and combining techniques [Internet]. Dordrecht: Springer; 2020 [cited 2025 Jan 10]. Available from: https://link.springer.com
20. Liu X, Rivera SC, Moher D. Reporting guidelines for clinical trial regulatory submissions [Internet]. London: BMJ Publishing Group; 2020 [cited 2025 Jan 10]. Available from: https://www.bmj.com
21. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence [Internet]. New York: Nature Publishing Group; 2019 [cited 2025 Jan 10]. Available from: https://www.nature.com
22. International Organization for Standardization. Artificial intelligence—risk management framework (ISO/IEC TR 24028) [Internet]. Geneva: ISO; 2020 [cited 2025 Jan 10]. Available from: https://www.iso.org
23. McKinsey & Company. AI-enabled quality and compliance in life sciences [Internet]. New York: McKinsey & Company; 2022 [cited 2025 Jan 10]. Available from:
https://www.mckinsey.com
24. Weber J, Gupta M. Strategic analytics in medical affairs [Internet]. London: SAGE Publications; 2021 [cited 2025 Jan 10]. Available from: https://journals.sagepub.com
25. Lee J, Kim H. Artificial intelligence–based optimization of field medical engagement [Internet]. London: BioMed Central; 2021 [cited 2025 Jan 10]. Available from:
https://bmcmedinformdecismak.biomedcentral.com
26. Accenture. Data-driven medical affairs: enabling field excellence through artificial intelligence [Internet]. Dublin: Accenture Life Sciences; 2022 [cited 2025 Jan 10]. Available from: https://www.accenture.com
27. Van der Velde F, Degens H. Generative artificial intelligence in regulatory medical writing [Internet]. Amsterdam: Elsevier; 2023 [cited 2025 Jan 10]. Available from:
https://www.sciencedirect.com
28. European Medicines Agency. Artificial intelligence in medicines regulation: opportunities and challenges [Internet]. Amsterdam: EMA; 2023 [cited 2025 Jan 10]. Available from: https://www.ema.europa.eu
29. Waring J, Lindvall C, Umeton R. Ethical considerations in artificial intelligence–supported healthcare content [Internet]. London: Nature Publishing Group; 2020 [cited 2025 Jan 10]. Available from: https://www.nature.com
30. Brynjolfsson E, McAfee A. Human–artificial intelligence collaboration in knowledge work [Internet]. Boston (MA): Harvard Business Publishing; 2021 [cited 2025 Jan 10]. Available from: https://hbr.org
31. European Commission. Ethics guidelines for trustworthy artificial intelligence [Internet]. Brussels: European Commission; 2020 [cited 2025 Jan 10]. Available from:
https://digital-strategy.ec.europa.eu
32. US Food and Drug Administration. Good machine learning practices for medical product development [Internet]. Silver Spring (MD): US FDA; 2021 [cited 2025 Jan 10]. Available from:
https://www.fda.gov
33. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine [Internet]. Hoboken (NJ): Wiley; 2019 [cited 2025 Jan 10]. Available from:
https://onlinelibrary.wiley.com
34. Sheller MJ, Reina GA, Edwards B, et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data [Internet]. London: Nature Publishing Group; 2020 [cited 2025 Jan 10]. Available from: https://www.nature.com
35. Coravos A, Khozin S, Mandl KD. Developing and adopting safe and effective digital biomarkers [Internet]. London: Nature Publishing Group; 2019 [cited 2025 Jan 10]. Available from: https://www.nature.com
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Ananthan S. Artificial Intelligence in Pharmaceutical Regulatory Affairs and Medical Science Liaison Activities: A Comprehensive Review. Int J Drug Reg Affairs [Internet]. 2025Dec.16 [cited 2026Jan.31];13(4):52-6. Available from: https://www.ijdra.com/index.php/journal/article/view/820