A Comparative Review of AI, IoT, and Big Data in Healthcare: Towards a Data-Centric Approach for Enhanced Data Quality and Contextual Adaptability

Abstract

The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data is revolutionizing healthcare by enabling predictive diagnostics, real-time monitoring, and personalized treatment through data-driven analytics and intelligent decision-making. Despite these advancements, the effectiveness of such systems is significantly hindered by poor data quality, including issues such as missing values, noise, bias, and inconsistencies. This study presents a systematic and comparative review of recent research at the intersection of AI, IoT, and Big Data in healthcare, highlighting critical gaps in data quality that undermine model performance and real-world reliability. In response, we introduce the Data-Centric AI (DCAI) paradigm as a promising approach focused on systematic data improvement rather than model complexity. We examine the application of the METRIC framework for assessing data quality dimensions such as completeness, consistency, fairness, and timeliness. Furthermore, we propose future research directions to improve scalability and trustworthiness in AI-driven healthcare, integrating advanced AI techniques such as generative AI and multimodal frameworks with DCAI principles for more ethical AI applications. This work serves as both a comparative synthesis of existing literature and a conceptual foundation for future experimental validation through a case study integrating context-aware data modeling and real-time decision support.

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