Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists

ABSTRACT

Introduction

The integration of artificial intelligence (AI) into clinical pharmacology could be a potential approach for accelerating drug discovery and development, improving patient care, and streamlining medical research processes.

Areas covered

We reviewed the current state of AI applications in clinical pharmacology, focusing on drug discovery and development, precision medicine, pharmacovigilance, and other ventures. Key AI applications in clinical pharmacology are examined, including machine learning, natural language processing, deep learning, and reinforcement learning etc. Additionally, the evolving role of clinical pharmacologists, ethical considerations, and challenges in implementing AI in clinical pharmacology are discussed.

Expert opinion

The AI could be instrumental in accelerating drug discovery, predicting drug safety and efficacy, and optimizing clinical trial designs. It can play a vital role in precision medicine by helping in personalized drug dosing, treatment selection, and predicting drug response based on genetic, clinical, and environmental factors. The role of AI in pharmacovigilance, such as signal detection and adverse event prediction, is also promising. The collaboration between clinical pharmacologists and AI experts also poses certain ethical and practical challenges. Clinical pharmacologists can be instrumental in shaping the future of AI-driven clinical pharmacology and contribute to the improvement of healthcare systems.

KEYWORDS:

• Clinical trial
• machine learning
• natural language processing
• pharmacovigilance
• precision medicine

Article highlights

• The integration of clinical pharmacology and artificial intelligence (AI) holds a great potential for improving patient care by enhancing drug discovery, personalizing medicine, and strengthening pharmacovigilance.

• AI tools such as natural language processing, deep learning and reinforcement learning, can further revolutionize certain key areas of clinical pharmacology.

• The article highlights the need for ongoing research, development, and collaboration of AI and clinical pharmacology, while also acknowledging the ethical and other challenges linked with it.

• We have reviewed the current state and prospects of AI-driven clinical pharmacology, discussing the benefits and challenges associated with its implementation.

• Clinical pharmacologists can play a crucial role in integrating AI with clinical Pharmacology, as by collaborating with AI experts, emphasizing the importance of interdisciplinary teamwork and addressing ethical considerations, to ensure the safe and effective use of AI in patient care.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.