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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

The transformative potential of neural networks in healthcare patient management systems

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  • The transformative potential of neural networks in healthcare patient management systems

Pavan Kumar Boyapati *

South Carolina Department of Health & Human Services, USA.

Review Article

World Journal of Advanced Research and Reviews, 2025, 26(01), 496-503

Article DOI: 10.30574/wjarr.2025.26.1.1055

DOI url: https://doi.org/10.30574/wjarr.2025.26.1.1055

Received on 25 February 2025; revised on 03 April 2025; accepted on 05 April 2025

Neural networks represent a transformative force in healthcare patient management systems through their revolutionary capabilities in pattern recognition, data analysis, and predictive modeling. These sophisticated computational frameworks offer solutions that range from enhanced diagnostic accuracy and personalized treatment planning to streamlined administrative processes and optimized resource allocation. The integration of neural networks with medical imaging, genomic sequencing, wearable devices, and electronic health records creates unprecedented opportunities for proactive interventions and continuous care monitoring. While challenges related to data quality, interpretability, privacy protection, and regulatory compliance must be addressed, the convergence of these technologies promises to fundamentally reshape healthcare delivery by improving clinical outcomes, reducing operational inefficiencies, and enabling truly patient-centered care models that extend beyond traditional clinical settings. 

Artificial Intelligence; Diagnostic Accuracy; Federated Learning; Personalized Medicine; Wearable Technology

https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-1055.pdf

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Pavan Kumar Boyapati. The transformative potential of neural networks in healthcare patient management systems. World Journal of Advanced Research and Reviews, 2025, 26(1), 496-503. Article DOI: https://doi.org/10.30574/wjarr.2025.26.1.1055

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


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