Abstract
In recent years, AI and quantum technologies have received a great deal of interest from a wide range of fields. This research introduces a novel methodology for creating AI systems that are both interpretable and clever, making them ideal for use in safe, trustworthy healthcare environments. In order to handle and analyse large healthcare datasets while maintaining privacy and security, our technology employs quantum optical neural networks (QONN). We place a premium on gathering useful healthcare data while strictly protecting individual privacy. The collected data undergoes meticulous cleaning and preprocessing, including normalization procedures to eliminate noise, outliers, and irrelevant information. The core of our approach involves the construction of a neural network utilizing both optical and quantum computing techniques. Key components of QONNs comprise qubits, optical elements, and conventional neural network layers. The training of the QONN is executed using pre-evaluated healthcare data, optimizing its performance through advanced techniques such as Improved Genetic Algorithms (IGA). Furthermore, we establish an AI system that employs explicit skill-based approaches. To achieve this, interpretability algorithms, saliency maps, and attention mechanisms may be essential tools. A critical aspect of this study involves a comprehensive evaluation of the AI system's performance. This evaluation includes soliciting feedback from qualified medical experts and implementing necessary enhancements and adjustments to augment its functionality and rectify any shortcomings. To assess the effectiveness of the constructed AI system, we conduct an analysis of pertinent metrics. We compare the system's results with those obtained using various healthcare analytics methods to ascertain its efficacy. This rigorous evaluation ensures that the AI system is not only functional but also a valuable asset in the realm of healthcare analytics and decision-making.
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TZ—Conceived and design the analysis. AT—Writing—Original draft preparation. MSJ, JLW—Collecting the Data, AM—Contributed data and analysis stools. JW—Performed and analysis—Wrote the Paper. KS—Editing and Figure Design.
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Zhou, T., Anuradha, T., Mahendra, S.J. et al. Efficient and economical smart healthcare application based on quantum optical neural network. Opt Quant Electron 56, 445 (2024). https://doi.org/10.1007/s11082-023-05853-y
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DOI: https://doi.org/10.1007/s11082-023-05853-y