DOI: 10.1111/bcp.15899 ISSN:

Artificial Intelligence in Clinical Pharmacology: A Case Study and Scoping Review of Large Language Models and Bioweapon Potential

Igor Rubinic, Marija Kurtov, Ivan Rubinic, Robert Likic, Paul I. Dargan, David M. Wood
  • Pharmacology (medical)
  • Pharmacology


This paper aims to explore the possibility of employing large language models (LLMs) – a type of artificial intelligence (AI) – in clinical pharmacology, with a focus on its possible misuse in bioweapon development. Additionally, ethical considerations, legislation, and potential risk reduction measures are analysed.


A comprehensive review of existing literature investigates the potential misuse of AI and LLMs in bioweapon creation. The search includes articles from PubMed, Scopus, and Web of Science Core Collection that were identified using a specific protocol. To explore the regulatory landscape, the platform was used.


he review highlights the dual‐use vulnerability of AI and LLMs, with a focus on bioweapon development. Subsequently, a case study is used to illustrate the potential of AI manipulation resulting in harmful substance synthesis. Existing regulations inadequately address the ethical concerns tied to AI and LLMs. Mitigation measures are proposed, including technical solutions (explainable AI), establishing ethical guidelines through collaborative efforts, and implementing policy changes to create a comprehensive regulatory framework.


The integration of AI and LLMs into clinical pharmacology presents invaluable opportunities, while also introducing significant ethical and safety considerations. Addressing the dual‐use nature of AI requires robust regulations, as well as adopting a strategic approach grounded in technical solutions and ethical values following the principles of transparency, accountability, and safety. Additionally, AI’s potential role in developing countermeasures against novel hazardous substances is underscored. By adopting a proactive approach, the potential benefits of AI and LLMs can be fully harnessed while minimizing the associated risks.

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