AI-Powered Computing Racks: Transforming Healthcare IT with Faster Diagnostics and Intelligent Data Processing
Healthcare IT underwent a revolution through artificial intelligence (AI) together with high-performance computing which particularly enhances diagnostics along with intelligent data processing operations. The use of AI-powered computing racks delivers exceptional speed alongside efficiency for handling large-scale medical data which leads to faster diagnoses and real-time patient observation and precise medical treatments. This paper studies how AI-powered computing racks redefine healthcare IT operations through their ability to boost computational power and generate more accurate diagnoses along with optimizing data management systems in hospital facilities and research facilities. The research uses actual medical studies together with machine learning methods and high-performance computing models to analyze how AI-powered racks affect medical IT infrastructure. It follows a quantitative data-oriented methodology. The study explores methods that these systems apply to maximize medical imaging analysis and electronic health records management while implementing advanced AI-based protection protocols to meet requirements from HIPAA and GDPR. AI-powered computing racks decrease diagnostic process durations by 40% while raising medical image precision to 30% and improving healthcare IT operational effectiveness by 45% compared to standard computing hardware solutions. The racks incorporate AI cybersecurity tools that both find irregularities and shield data infrastructure from cyber dangers to maintain secure database operations. The study enhances AI in healthcare IT knowledge while developing guidelines for hospital and research facility integration of AI-powered computing racks. This study introduces novel research through its real-time data processing system design along with deployment potential which leads to better healthcare operational efficiency and improved patient results.
Authors
- Md. Easin Arafat (ORCID: https://orcid.org/0000-0003-4014-9144)
- Mir Abrar Hossain
- K. A. Desai (ORCID: https://orcid.org/0000-0001-5103-9480)
- Sharmin Akter (ORCID: https://orcid.org/0000-0002-1695-2052)
- A. Asha
Institutions
- American International College (US)
- St. Francis College (US)
- Campbellsville University (US)
Publication Details
- Journal
- The American Journal of Applied Sciences
- Published
- 2025-05-06
- DOI
- https://doi.org/10.37547/tajas/volume07issue05-02
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00