Authors:
Sudheer Panyaram

Addresses:
Department of ERP Applications, Fisker, California, United States of America. sudheer5940@gmail.com

Abstract:

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.

Keywords: Quantum Computing; Artificial Intelligence; Predictive Analytics; Personalized Medicine; Quantum Neural Networks (QNNs); Quantum Support Vector Machines(QSVMs).

Received on: 11/08/2023, Revised on: 05/11/2023, Accepted on: 04/01/2024, Published on: 01/03/2024

DOI: 10.69888/FTSCS.2024.000194

FMDB Transactions on Sustainable Computing Systems, 2024 Vol. 2 No. 1, Pages: 22-31

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