Artificial Intelligence and Deep Learning in Healthcare

Advances in drug research are made possible by artificial intelligence techniques. Numerous obstacles and hurdles, including poor effectiveness, off-target delivery, lengthy development times, and high costs, affect drug research and development. Moreover, a huge amount of complicated data sets that are available from proteomics, genomes, clinical trials, and microarrays block the procedure of drug evolution. In this sense, Artificial intelligence (AI) and deep learning (DL) are critical components of the drug development process. There are several drug discovery methods, such as structure-based virtual screening and ligand-based virtual screening, pharmacophore modeling, polypharmacy prediction of toxicity, based on virtual screening, peptide synthesis, drug monitoring, drug release, drug repositioning, physicochemical activity, and activity quantitative-based structure have been developed. This chapter describes the function of AI and DL in drug discovery. It also covers how many applications, what tools are being used, and what obstacles AI and DL are facing. In short, the chapter discusses the rise of AI and DL in drug development and healthcare data analytics. Finally, it discusses the encounters of artificial intelligence and deep learning models with the aspect of future vision for developing rural health.