Cloud-based solutions for sustainable biomedical data processing and management

Abstract

The rapid advancement of big data analytics is beginning to significantly impact the progress of medical research and practice, enabling data-driven decision-making and innovation. It has led to the creation of tools for collecting, organizing, processing, and integrating vast amounts of diverse, structured, and unstructured data produced by today’s healthcare systems across hospitals, labs, and telemedicine platforms. Recently, big data analytics has been utilized to assist in the study of infections like COVID-19 and the delivery of personalized, efficient care. Nevertheless, several fundamental challenges associated with big data—such as ethical concerns, data silos, and algorithmic biases—continue to hinder the adoption rate and the advancement of research in this area. In this work, we address some of these key issues, concentrating on three developing and very hopeful fields in medical research: genomics-based analytics, AI-enhanced image, and signal processing. This field also explores potential research areas such as federated learning and edge computing that could significantly improve healthcare delivery while ensuring patient privacy.