The Impact of Big Data and Cloud Computing on Genetic Testing and Reproductive Health Management
DOI:
https://doi.org/10.69980/ajpr.v28i1.69Keywords:
Big Data, Cloud Computing, Genetic Testing, Reproductive Health, Personalized Medicine, Data Analytics, Healthcare Innovation, Genetic Data Storage, Predictive Modeling, Reproductive Medicine, Health Data Integration, Artificial Intelligence, Bioinformatics, Cost Reduction in Healthcare, Precision Medicine.Abstract
The rapid advancements in Big Data and Cloud Computing have revolutionized various sectors, with healthcare being one of the most significantly impacted domains. In the context of genetic testing and reproductive health management, these technologies offer transformative potential by improving data storage, accessibility, and processing capabilities. Big Data analytics enable the analysis of vast and complex genetic datasets, leading to more accurate predictive models for disease risk, personalized medicine, and improved reproductive outcomes. Cloud computing enhances the scalability and flexibility of data storage, providing seamless integration of diverse health information across different platforms, and facilitating real-time access to genetic and medical data. This synergy between Big Data and Cloud Computing not only optimizes the efficiency of genetic testing but also supports the delivery of personalized care in reproductive health, allowing for timely interventions and informed decision-making. Furthermore, the reduced cost of infrastructure and the increased automation of genetic analysis processes contribute to making advanced genetic testing more accessible to a broader population. This paper explores the multifaceted impact of Big Data and Cloud Computing on genetic testing, their role in reproductive health management, and the implications for future healthcare practices. It highlights the potential benefits, challenges, and ethical considerations of incorporating these technologies into reproductive health systems, ultimately offering a vision for a more precise and efficient approach to genetic testing and personalized reproductive care.
References
[1] Polineni, T. N. S., & Seenu, A. (2025). The New Frontier of Healthcare and Industry: Subash’s Expertise in Big Data and Cloud Computing for Enhanced Operational Efficiency. Cuestiones de Fisioterapia, 54(2), 271-283.
[2] Maguluri, K. K., Ganti, V. K. A. T., Yasmeen, Z., & Pandugula, C. (2025, January). Progressive GAN Framework for Realistic Chest X-Ray Synthesis and Data Augmentation. In 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) (pp. 755-760). IEEE.
[3] Koppolu, H. K. R. Deep Learning and Agentic AI for Automated Payment Fraud Detection: Enhancing Merchant Services Through Predictive Intelligence.
[4] Nampalli, R. C. R., & Adusupalli, B. (2024). AI-Driven Neural Networks for Real-Time Passenger Flow Optimization in High-Speed Rail Networks. Nanotechnology Perceptions, 334-348.
[5] Chakilam, C. (2022). Generative AI-Driven Frameworks for Streamlining Patient Education and Treatment Logistics in Complex Healthcare Ecosystems. Kurdish Studies. Green Publication. https://doi. org/10.53555/ks. v10i2, 3719.
[6] Sriram, H. K. (2023). Harnessing AI Neural Networks and Generative AI for Advanced Customer Engagement: Insights into Loyalty Programs, Marketing Automation, and Real-Time Analytics. Educational Administration: Theory and Practice, 29(4), 4361-4374.
[7] Burugulla, J. K. R. (2025). Enhancing Credit and Charge Card Risk Assessment Through Generative AI and Big Data Analytics: A Novel Approach to Fraud Detection and Consumer Spending Patterns. Cuestiones de Fisioterapia, 54(4), 964-972.
[8] Chava K. Dynamic Neural Architectures and AI-Augmented Platforms for Personalized Direct-to-Practitioner Healthcare Engagements. J Neonatal Surg [Internet]. 2025Feb.24 [cited 2025Mar.24];14(4S):501-10. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/1824
[9] Challa, K. (2024). Neural Networks in Inclusive Financial Systems: Generative AI for Bridging the Gap Between Technology and Socioeconomic Equity. MSW Management Journal, 34(2), 749-763.
[10] Sondinti, K., & Reddy, L. (2025). The Future of Customer Engagement in Retail Banking: Exploring the Potential of Augmented Reality and Immersive Technologies. Available at SSRN 5136025.
[11] Malempati, M., & Rani, P. S. Autonomous AI Ecosystems for Seamless Digital Transactions: Exploring Neural Network-Enhanced Predictive Payment Models.
[12] Pallav Kumar Kaulwar. (2023). Tax Optimization and Compliance in Global Business Operations: Analyzing the Challenges and Opportunities of International Taxation Policies and Transfer Pricing. International Journal of Finance (IJFIN) - ABDC Journal Quality List, 36(6), 150-181.
[13] Vankayalapati, R. K. (2025). Architectural foundations of hybrid cloud. The Synergy Between Public and Private Clouds in Hybrid Infrastructure Models: Real-World Case Studies and Best Practices, 17.
[14] Nuka, S. T. (2025). Leveraging AI and Generative AI for Medical Device Innovation: Enhancing Custom Product Development and Patient Specific Solutions. Journal of Neonatal Surgery, 14(4s).
[15] Rao Suura S. Agentic AI Systems in Organ Health Management: Early Detection of Rejection in Transplant Patients. J Neonatal Surg [Internet]. 2025Feb.24 [cited 2025Mar.24];14(4S):490-50.
[16] Kannan, S. (2025). Transforming Community Engagement with Generative AI: Harnessing Machine Learning and Neural Networks for Hunger Alleviation and Global Food Security. Cuestiones de Fisioterapia, 54(4), 953-963.
[17] Srinivas Kalisetty, D. A. S. Leveraging Artificial Intelligence and Machine Learning for Predictive Bid Analysis in Supply Chain Management: A Data-Driven Approach to Optimize Procurement Strategies.
[18] Challa, S. R. Diversification in Investment Portfolios: Evaluating the Performance of Mutual Funds, ETFs, and Fixed Income Securities in Volatile Markets.
[19] Vamsee Pamisetty. (2023). Intelligent Financial Governance: The Role of AI and Machine Learning in Enhancing Fiscal Impact Analysis and Budget Forecasting for Government Entities. Journal for ReAttach Therapy and Developmental Diversities, 6(10s(2), 1785–1796. https://doi.org/10.53555/jrtdd.v6i10s(2).3480
[20] Komaragiri, V. B. (2022). AI-Driven Maintenance Algorithms For Intelligent Network Systems: Leveraging Neural Networks To Predict And Optimize Performance In Dynamic Environments. Migration Letters, 19, 1949-1964.
[21 Annapareddy, V. N., & Rani, P. S. AI and ML Applications in RealTime Energy Monitoring and Optimization for Residential Solar Power Systems.
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