AI-Driven Total Quality Management In Healthcare Technology: Integrating Medical Image Processing, Machine Learning, And Organizational Benchmarking

Abstract

Artificial intelligence (AI) has become one of the revolutionary technologies to enhance healthcare quality, accuracy of diagnoses, and efficiency of the medical system in the modern health care. In this research, an AI-enabled Total Quality Management (TQM) system is suggested, which combines the medical image processing, machine learning algorithms, and benchmarking of the organization to improve the intelligent healthcare technology. The suggested framework integrates the medical image analysis based on deep learning with predictive medical care quality estimation to aid in correct diagnosis, workflow optimization, and organizational performance analysis. Automated medical image classification and anomaly detection were applied using Convolutional Neural Networks (CNNs) and hybrid machine learning models were applied to predict healthcare quality and benchmark healthcare operations. The framework also includes the organizational performance indicators like diagnostic accuracy, patient satisfaction, treatment efficiency, and resource use to achieve a comprehensive quality assessment mechanism. Experimental investigation using various medical images datasets and health care operating datasets was done. The suggested framework had an excellent diagnostic performance and a high classification accuracy of more than 97% as well as low processing time and better quality metrics in an organization in comparison to traditional healthcare management models. The robustness, reliability and the ability to generalize the suggested system were statistically validated by 10-fold cross-validation and paired t-test analysis (p < 0.05). The established AI-based TQM system can be effectively used to assist smart clinical decision-making, ongoing healthcare quality enhancement, and managed healthcare technology administration in contemporary healthcare organizations.