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

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Pubblicato in:The Journal of Engineering vol. 2025, no. 1 (Jan/Dec 2025)
Autore principale: Akhtar, Zarif Bin
Pubblicazione:
John Wiley & Sons, Inc.
Soggetti:
Medical diagnosis
Scientific visualization
Data processing
Collaboration
Internet of Things
Artificial intelligence
Health care
Prediction models
Data science
Clinical medicine
Public health
Cardiology
Decision making
Biomedical engineering
Data integration
Ethics
Machine learning
Natural language processing
Customization
Electronic health records
Radiology
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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.
ISSN:2051-3305
DOI:10.1049/tje2.70137
Fonte:Advanced Technologies & Aerospace Database