Hybrid Machine Learning And Quantum Computing Approaches For Healthcare Organization Management And Automated Medical Image Diagnostics

Abstract

The genius of the heavily growing healthcare data and the growing need to make correct clinical diagnosis has increased the pace of introducing the use of artificial intelligence and sophisticated computational technologies in the contemporary healthcare systems. This work suggests a multi-technology machine learning and quantum computing platform to manage healthcare organizations and conduct automated diagnostics of medical images. The suggested system will combine convolutional neural networks, quantum-inspired optimization algorithms, and intelligent analytics in healthcare to improve the accuracy of the diagnosis, the efficiency of its operations, and clinical decision-making. Data sets of chest X-ray imaging were used and health care organization datasets were used as experimental data. The framework utilized feature deep extraction, probabilistic quantum-inspired feature optimization and hybrid classification techniques to identify diseases and analyse health resource using automated detection and classification techniques. Preprocessing data methods such as normalization, augmentation, and contrast enhancement were used to enhance the model robustness and generalization. As it was shown in the experiments, the proposed framework showed high performance in diagnostic and organizational performance indicators in comparison with the traditional machine learning techniques. The developed model achieved an accuracy of 96.8%, precision of 96.1%, recall of 95.7%, F1-score of 95.9%, and ROC-AUC of 97.2%. The reliability and stability of the proposed architecture was statistically validated with the help of 10-fold cross-validation. The intelligent framework developed can play a significant role in automated clinical diagnostics, optimization of healthcare workflow, intelligent hospital management, and scalable AI-assisted healthcare systems to modern healthcare settings.