Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence’, ‘Pharmaceutical research’, ‘drug discovery’, ‘clinical trial’, ‘disease diagnosis’, etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public.
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The risk of type 2 diabetes mellitus (T2DM) is increasing abundantly due to lifestyle-related obesity and associated cardiovascular problems. Presently, Glycogen synthase kinase-3 (GSK-3) has gained considerable attention from biomedical scientists to treat diabetes. Phosphorylation of GSK-3 permits a number of cellular activities like regulation of cell signaling, cellular metabolism, cell proliferation and cellular transport. Inhibiting GSK-3 activity by pharmacological intervention has become an important strategy for the management of T2DM. This review focuses on the schematic representation of fundamental GSK-3 enzymology and encompasses the GSK-3 inhibitors as a future therapeutic lead target for the management of T2DM that may significantly regulate insulin sensitivity to insulin receptor, glycogen synthesis and glucose metabolism. The various signaling mechanisms of inhibiting the GSK-3 by describing insulin signaling through Insulin Receptor Substrate (IRS-1), Phosphatidylinositol-3 Kinase (PI3K) and Protein Kinase B (PKB/ AKT) pathways that may hopefully facilitate the pharmacologist to design for antidiabetic drug evaluation model in near future have also been highlighted.
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