Leveraging AI and Big Data in Low-Resource Healthcare Settings

Abstract

Big data and artificial intelligence are game-changing technologies for the underdeveloped healthcare industry because they help optimize the entire supply chain and deliver more exact patient outcome information. Machine learning approaches that have recently seen more growing popularity include deep learning models that have brought revolution within the healthcare system in the previous years due to more complicated data compared to previous years . Machine learning is an essential data analysis procedure to describe efficient and effective methods to extract hidden information from large amounts of data that it would take logical analytics too long to manage. Recent years have seen an expansion and growth of advanced intelligent systems that have been able to learn more about clinical treatments and glean untapped medical information emanating from vast quantities of data when it comes to drug discovery and chemistry. The aim of this chapter is, therefore, to assess which big data and artificial intelligence approaches are prevalent in healthcare systems by investigating the most advanced big data structures, applications, and industry trends today available. First and foremost, the purpose is to provide a comprehensive overview of how the artificial intelligence and big data models can allocation in healthcare solutions fill the gap between machine learning approaches’ lack of human coverage and the healthcare data’s complexity. Moreover, current artificial intelligence technologies, including generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, are also increasingly being used for drug discovery and chemistry . Finally, the work presents the existing open challenges and the future directions in the drug formulation development field. To this end, the review will cover on published algorithms/automation tools for artificial intelligence applied to large scale-data in the case of healthcare .