AI-Driven Decision-Making Applications in Pharmaceutical Sciences

Introduction

The field of pharmaceutical sciences has traditionally placed great emphasis on studying decision-making processes to advance research. This promotes development and ensures the delivery of safe and effective medications to patients. However, in recent years, there has been a notable shift in this paradigm. This change is attributed to researchers integrating artificial intelligence (AI) into decision-making procedures, bringing about transformative effects not only in the pharmaceutical sector but also across various industries (Carou-Senra et al., 2023; Liu & Rudd, 2023; Malviya et al., 2023; Singh Sharma, 2023). AI-driven decision-making applications have emerged as powerful tools, enabling pharmaceutical scientists to make more informed and efficient decisions. The repercussions of this are evident in the accelerated pace of drug discovery, its optimization, and the enhancement of manufacturing processes (El-Naggar et al., 2023). Figure 1 offers a comprehensive visual representation of the key disciplines within the pharmaceutical sciences that AI-driven decision-making applications have profoundly impacted. The influence of AI is most pronounced in six primary areas: drug research and discovery, clinical development, personalized medicines, manufacturing and supply chain, launch and commercial activities, and postmarketing surveillance. AI-powered applications not only enhance decision-making but also elevate patient outcomes and improve the overall performance of the pharmaceutical industry. This highlights AI’s transformative potential, positioning it as a pivotal catalyst for the future evolution of pharmaceutical sciences.

Figure 1.

Areas of AI-driven decision-making applications in pharmaceutical sciences