Implications of Big Data Analytics, AI, ML, and DL in Bangladesh’s Healthcare System

Abstract

This scoping review explores the intersection of big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL) within the healthcare ecosystem of Bangladesh. The study identifies key applications such as disease outbreak prediction, patient monitoring, and medical image processing, which are increasingly powered by big data technologies. Despite technological advancements, significant barriers remain, including lack of infrastructure, insufficient training, and concerns over data security. The authors examine current initiatives by both public and private healthcare institutions and categorize them based on technological readiness and effectiveness. The review further highlights the need for interoperability standards and regulatory frameworks to ensure ethical use of patient data. Moreover, the paper emphasizes the potential of AI and big data to bridge the urban-rural healthcare divide by enabling remote diagnostics and personalized treatment plans. Through a multi-stakeholder analysis, including policy makers, healthcare providers, and IT vendors, the study proposes a strategic roadmap for nationwide digital transformation. The authors conclude that a well-coordinated approach involving capacity building, data governance, and public-private partnerships is essential to harness the full potential of big data in the healthcare domain of developing nations like Bangladesh.