Artificial Intelligence in Regulatory Medical Writing: Opportunities, Challenges, and Implications for Clinical Trial Documentation
Abstract:
Artificial intelligence (AI) is increasingly being explored in healthcare communication and scientific documentation, including applications in regulatory medical writing. Regulatory medical writing plays a critical role in the preparation of clinical trial documentation required for regulatory submissions, including clinical study reports, study protocols, investigator brochures, and safety reports. The integration of AI technologies, particularly natural language processing and machine learning, has created opportunities to improve efficiency, consistency, and workflow management in documentation-related activities. However, concerns remain regarding accuracy, transparency, ethical considerations, and regulatory compliance associated with AI-assisted documentation. This article explores the current and potential applications of artificial intelligence in regulatory medical writing and evaluates its implications for clinical trial documentation. A qualitative narrative review approach was adopted using published literature, regulatory guidance documents, and policy papers related to AI in healthcare, scientific communication, and regulatory science. The review identifies major opportunities associated with AI-assisted documentation, including automated drafting, content summarization, workflow optimization, and consistency in document preparation. At the same time, important limitations and challenges were identified, including reliability concerns, hallucination risks, lack of transparency, ethical considerations, data privacy issues, and uncertainties regarding regulatory acceptance. The review further discusses the evolving role of medical writers in an AI-assisted environment and highlights the importance of human oversight in maintaining documentation quality, scientific accuracy, and compliance with regulatory expectations. The findings suggest that while AI has significant potential to support regulatory medical writing, responsible integration requires robust governance frameworks, ethical oversight, and clear regulatory guidance.References:
[1]. T, Kalakota
R. The potential for artificial intelligence in healthcare. Future Healthc J.
2019;6(2):94-8.
[2]. Topol EJ.
High-performance medicine: the convergence of human and artificial
intelligence. Nat Med. 2019;25(1):44-56.
[3]. International
Council for Harmonisation of Technical Requirements for Pharmaceuticals for
Human Use (ICH). ICH harmonised guideline E3: Structure and content of clinical
study reports. 1995.
[4]. International
Council for Harmonisation of Technical Requirements for Pharmaceuticals for
Human Use (ICH). ICH harmonised guideline E6(R3): Good Clinical Practice. 2025.
[5]. World Health
Organization. Ethics and governance of artificial intelligence for health.
Geneva: World Health Organization; 2021.
[6]. European
Medicines Agency. Reflection paper on the use of artificial intelligence in the
medicinal product lifecycle. Amsterdam: European Medicines Agency; 2024.
[7]. Gattrell WT,
Hopewell S, Young K, et al. Professional medical writing support and the
quality of randomised controlled trial reporting: a cross-sectional study. BMJ
Open. 2016;6(2):e010329.
[8]. Biswas S.
ChatGPT and the future of medical writing. Radiology. 2023;307(2):e223312.
[9]. Jiang
F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present
and future. Stroke Vasc Neurol. 2017;2(4):230-43.
[10]. Kelly CJ,
Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for
delivering clinical impact with artificial intelligence. BMC Med.
2019;17(1):195.
[11]. Yu KH, Beam
AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng.
2018;2(10):719-31.
[12]. Esteva A,
Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat
Med. 2019;25(1):24-9.
[13]. Price WN,
Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37-43.
[14]. Goodman KW.
Ethics, medicine, and information technology: intelligent machines and the
transformation of healthcare. Cambridge: Cambridge University Press; 2020.
[15]. Shah P,
Kendall F, Khozin S, et al. Artificial intelligence and machine learning in
clinical development: a translational perspective. NPJ Digit Med. 2019;2(1):69.
[16]. Wang F,
Casalino LP, Khullar D. Deep learning in medicine—promise, progress, and
challenges. JAMA Intern Med. 2019;179(3):293-4.
[17]. Luxton DD.
Artificial intelligence in psychological practice: current and future
applications and implications. Prof Psychol. 2016;47(3):147-53.
[18]. Sendak MP,
D'Arcy J, Kashyap S, et al. A path for translation of machine learning products
into healthcare delivery. EMJ Innov. 2020;4(1):25-32.
[19]. Shortliffe
EH, Sepúlveda MJ. Clinical decision support in the era of artificial
intelligence. JAMA. 2018;320(21):2199-200.
[20]. Mesko B. The
role of artificial intelligence in precision medicine. Expert Rev Precis Med
Drug Dev. 2017;2(5):239-41.
[21]. World Health
Organization. Ethics and governance of artificial intelligence for health:
guidance on generative artificial intelligence. Geneva: World Health
Organization; 2024.
[22]. Sallam M.
ChatGPT utility in healthcare education, research, and practice: systematic
review on the promising perspectives and valid concerns. Healthcare.
2023;11(6):887.
[23]. Cascella M,
Montomoli J, Bellini V, Bignami E. Evaluating the feasibility of ChatGPT in
healthcare: an analysis of multiple clinical and research scenarios. J Med
Syst. 2023;47(1):33.
[24]. Biswas S. Role of generative artificial intelligence in medical writing and scientific communication. Med Writ. 2024;33(1):15-22.
