Role of Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaco-epidemiology: A Narrative Review

Abstract

A crucial area of study called pharmaco-epidemiology combines pharmacology and epidemiology to investigate how pharmaceuticals are used, work, and if they are safe in  a huge population. Clinical studies, pre-clinical trials, and medication research and development are just a few of the pharmaco-epidemiology research areas that could benefit from the application of artificial intelligence (AI) and machine learning (ML). Huge volumes of patient’s data can be analyzed using AI and ML to pinpoint patient’s subpopulations that are more likely to benefit from specific pharmaceutical treatments. Researchers  have the ability to develop more tailored treatment plans that improve patient’s outcomes by supplying this insight. Additionally, AI and ML can assist in hastening the discovery of new drugs and reducing development costs by helping to identify a potential responder early on in the clinical trial process. Pre-clinical studies using AI and ML can be more productive, less expensive, and less time consuming to create, all of which can increase the likelihood  of medication approval. These methods are also useful in the initial screening of pharmacological compounds and the biological success rate prediction stages of early drug research. Overall, AI and ML are crucial to many aspects of pharmaco-epidemiology clinical research and medication development, enhancing safety in clinical research by enabling the detection of safety hazards and adverse reactions, while also improving patient’s outcomes and offering more individualized treatment plans.