Articles
| Open Access |
DOI: AI-Powered Computing Racks: Transforming Healthcare IT with Faster Diagnostics and Intelligent Data Processing
Sharmin Akter , Department of Information Technology in Project Management, St. Francis College, Brooklyn, New York, USA MD Sheam Arafat , Department of Master Business Administration in Business Analytics, International American University, Los Angeles, California, USA Kirtibhai Desai , Master of Science in Computer Science, Campbellsville University USA Mir Abrar Hossain , Department of Master Business Administration in Business Analytics, International American University, Los Angeles, California, USA Ayesha Islam Asha , Department of Master Business Administration, International American University, Los Angeles, California, USAAbstract
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.
Keywords
AI-powered computing racks, healthcare IT, intelligent data processing, medical diagnostics, real-time computing
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Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-1219.
Ashley EA. The precision medicine initiative: a new national effort. JAMA. 2015;313(21):2119-2120.
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118.
Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities, and challenges. Brief Bioinform. 2018;19(6):1236-1246.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
Wang Y, Kung L, Byrd TA. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol Forecast Soc Change. 2018;126:3-13.
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present, and future. Stroke Vasc Neurol. 2017;2(4):230-243.
Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2:3.
Chen M, Hao Y, Hwang K, et al. Disease prediction by machine learning over big data from healthcare communities. IEEE Access. 2017;5:8869-8879.
Bates DW, Saria S, Ohno-Machado L, et al. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood). 2014;33(7):1123-1131.
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Ashley EA. The precision medicine initiative: a new national effort. JAMA. 2015;313(21):2119-2120.
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present, and future. Stroke Vasc Neurol. 2017;2(4):230-243.
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