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Computación y Sistemas

Integration of Agile Approaches with Quantum High-Performance Computing in Healthcare System Designs

Abdullah Abdullah, Nida Hafeez, Kinza Sardar, José Luis Oropeza Rodríguez, Alexander F. Gelbukh, Grigori Sidorov

Published Sep 30, 2025• 0 citations
Publisher

Abstract

This study explores the integration of Agile methodologies and quantum high-performance computing (HPC) in healthcare. Agile methodology offers flexibility, while quantum HPC has the potential to revolutionize healthcare. The paper presents an architectural design for HPC in healthcare, discussing tool alignment, software development, and integration challenges. It emphasizes user-centered design, cross-functional teams, continuous integration, and retrospectives for software development. Personalized medical tools leverage data analytics and machine learning (ML) with quantum HPC for improved speed and accuracy. Real-time patient monitoring systems utilize wearable devices and sensors, developed iteratively using Agile methodology. The integration of Agile methodologies with quantum HPC can transform healthcare, improving access, efficiency, and patient outcomes. The study focuses on developing personalized medicine, real-time monitoring, and telemedicine tools to ensure improved care, security, and privacy through quantum HPC advancements.

DOI
10.13053/cys-29-3-5810

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This study explores the integration of Agile methodologies with quantum high-performance computing (HPC) in healthcare. It proposes an architecture that supports user-centred design, iterative development, and cross-functional teams. The approach enhances software for personalised medicine, real-time monitoring, and telemedicine by combining Agile practices with quantum HPC’s speed and accuracy. Wearable devices and sensors support continuous patient monitoring, while data analytics and machine learning improve outcomes. The integration aims to boost efficiency, access, and care quality, ensuring better security and privacy in healthcare systems.
Advancements in quantum computing hold the potential to revolutionize artificial intelligence (AI), particularly in the field of healthcare. This paper explores how quantum computing can be leveraged to improve predictive analytics and facilitate personalized medicine. Through enhanced computational capacity, quantum computing enables faster processing and analysis of large, complex datasets, essential for predictive models in healthcare. This integration can lead to more precise diagnostics, treatment options, and disease prevention strategies by refining AI’s capability to handle vast data. Quantum algorithms such as quantum neural networks and quantum support vector machines can significantly optimize machine learning models, reducing the time required for data processing and improving predictive accuracy. Personalized medicine, an emerging trend in healthcare, relies heavily on detailed patient data to tailor treatments. The combination of quantum computing and AI promises to make this process faster, more accurate, and scalable. This study presents a detailed review of the current state of quantum AI in healthcare, highlights methodologies for data analysis, and showcases a comprehensive architecture for integrating quantum computing in AI healthcare systems. We analyse relevant data from case studies to demonstrate the advantages and challenges of adopting quantum computing for AI-driven healthcare applications.
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Quantum computing presents a paradigm shift in computational capabilities, offering unknown possibilities for revolutionizing healthcare operations. This exploration paper aims to explore the impact of quantum computing on healthcare, focusing on its essential elements, understanding the fundamentals, examining its algorithms applicable to healthcare, and assessing their practical operations in the field. The methodology involves a comprehensive review of the literature, including quantum computing principles, machine learning algorithms, and healthcare use cases. Findings indicate that quantum algorithms have the potential to significantly accelerate drug discovery, enable personalized medications, and improve individual imaging. However, challenges such as error rates and scalability issues need to be addressed. The counterarguments to this exploration encompass ethical considerations surrounding data security, privacy, and responsible quantum computing practices in healthcare. As quantum computing advances, this paper suggests a transformative future for healthcare, where quantum technologies play a vital part in optimizing treatments, perfecting patient issues, and advancing medical exploration.
The integration of quantum computing and artificial intelligence (AI) holds transformative potential for healthcare by enabling adaptive diagnostic models that evolve with dynamic patient data and expanding medical knowledge. Leveraging quantum principles such as superposition and entanglement allows these models to efficiently process complex, high-dimensional healthcare datasets, improving precision in disease diagnosis and personalized treatment strategies. The proposed hybrid architecture combines quantum machine learning algorithms with classical AI components for interpretability and continuous refinement through real-time data ingestion, mitigating model drift and maintaining clinical relevance. This approach demonstrates significant promise in enhancing diagnostic accuracy and fostering intelligent healthcare solutions responsive to individual patient needs and medical advances [1]– [3].