Artificial intelligence in clinical pharmacology: A case study and scoping review of large language models and bioweapon potential

Abstract

AimThis 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.MethodsA 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 OECD.ai platform was used.Resultshe 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.ConclusionThe 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.

Social acceptance of artificial intelligence (AI) application for improving medical service diagnostics

“…It involves an examination of the current state of AI applications in pharmacy, identifying gaps and challenges, and suggesting potential strategies for integrating AI tools into daily practice. This integration has the potential to propel the pharmaceutical sector into a new era of healthcare, characterized by increased personalization and efficiency Rubinic et. al., 2023 Exploring 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 Healthcare and Healthcare Company Management AI systems can support healthcare organizations in optimizing processes, detecting abuses, and reducing care costs Dicuonzo, Donofrio, Fusco, & Shini, 2023 Investigating how AI supports the effective and efficient management of the healthcare system by examining the Humber River Hospital in Toronto using the case study methodology Kulkov, 2021 Identifying exactly how AI affects the key and supporting business processes of pharmaceutical companies Cubric, 2020 Review of AI implementations in various business sectors (including healthcare)…”

The British Journal of Clinical Pharmacology: The first 50 years

“…And it is what I intend to do until I pass the baton to my successor. During my tenure we have been making inroads into newer areas, such as Equity in Precision Medicine,43,44 Advanced Therapeutic Medicinal Products,22 Climate Change Pharmacology,45 and Artificial Intelligence,[46][47][48] and I expect the next EiC to lead us further into these interesting new areas, as well as areas that we have not even thought of. I also hope that the next EiC will continue the Issue Highlights, and that there will be less need to address epidemics, or the atrocities of armed conflicts, or other disasters.…”