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Download PDFOpen PDF in browserQuantum Computing for Enhancing AI Models in Healthcare Diagnostics: a Theoretical PerspectiveEasyChair Preprint 153554 pages•Date: November 1, 2024Abstract Artificial intelligence (AI) has brought transformative potential to healthcare, with its uses extending from diagnostics to personalized care. However, traditional AI models, including deep learning networks, face significant challenges in computational demand, data complexity, and pro- cessing speed. Quantum computing, with its excep- tional computational power, offers a promising solu- tion. This paper examines how quantum computing can enhance AI models in healthcare diagnostics. Through analyzing algorithms like Quantum Neural Networks (QNNs) and Quantum Approximate Opti- mization Algorithm (QAOA), we provide a theoreti- cal perspective on the potential for improvements in diagnostic accuracy, efficiency, and scalability. The paper highlights the constraints of classical AI models and how quantum technology could overcome these limitations, providing new directions for research into quantum-powered AI in healthcare Keyphrases: Artificial Intelligence, Healthcare Diagnostics, quantum computing Download PDFOpen PDF in browser |
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