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\begin{document}

\title{AI and machine learning for clinical pharmacology: A Primer}



\author[1]{David Ryan}%
\author[1]{Rory  Maclean}%
\author[2]{Alfred  Balston }%
\author[1]{Andrew Scourfield}%
\author[1]{Anoop Shah}%
\author[3]{Jack Ross}%
\affil[1]{University College London Hospitals NHS Foundation Trust}%
\affil[2]{Guy's and St Thomas' NHS Foundation Trust}%
\affil[3]{University College Hospital}%


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  \date{May 23, 2024}


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\begin{abstract}
Artificial intelligence (AI) will impact many aspects of clinical
pharmacology including drug discovery and development, clinical trials,
personalised medicine, pharmacogenomics, pharmacovigilance and clinical
toxicology. The rapid progress of AI in healthcare means clinical
pharmacologists should have an understanding of AI and its
implementation into clinical practice. As with any new therapy or health
technology, it is imperative that AI tools are subject to robust and
stringent evaluation to ensure that they enhance clinical practice in a
safe and equitable manner. This review serves as an introduction to AI
for the clinical pharmacologist, highlighting current applications,
aspects of model development and issues surrounding evaluation and
deployment. The aim of this article is to empower clinical
pharmacologists to embrace and lead on the safe and effective use of AI
within healthcare.%
\end{abstract}%



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