Data‐Driven Healthcare Innovations: An Inclusive Investigative Exploration Into Artificial Intelligence (AI), Machine Learning (ML), Extended Reality (XR) and Internet of Things (IoT) Technologies

Abstract

ABSTRACT

Health data science serves as a transformative bridge between healthcare and technology, enabling data‐driven decision‐making, personalised medicine, and more effective public health interventions. This study presents a comprehensive investigation into advanced techniques such as machine learning (ML), natural language processing (NLP), predictive analytics, and data visualisation, emphasising their applications in oncology, diabetes management, radiology, cardiology, and public health. High‐quality datasets—sourced from electronic health records (EHRs), national health surveys, and clinical trial databases—were rigorously preprocessed to ensure accuracy and reliability. The interdisciplinary approach integrates expertise from computer science, statistics, biomedical engineering, and clinical medicine to foster cross‐sector collaboration. Real‐world case studies demonstrate measurable benefits, including up to a 20% improvement in early cancer detection accuracy using predictive models, a 15% reduction in diagnostic errors via AI‐assisted radiology, and enhanced personalised treatment pathways for chronic disease management. The findings underscore Health Data Science's role in evidence‐based policy‐making, illustrated by data‐driven strategies for pandemic response planning. Ethical and security considerations are addressed through compliance with the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), alongside emerging concerns over cyber risks, transparency, fairness, and accountability in AI systems. Limitations such as data integration challenges and institutional resistance are discussed, with proposed solutions. Future research directions include real‐time data processing, improved interoperability with EHR systems, and broader deployment of predictive models to enhance patient outcomes and healthcare efficiency.