Healthcare revolution: AI applications transforming fuzzy-based operations research

Abstract

The fuzzy-based use of Artificial Intelligence (AI) in healthcare could change operations research. There is a possibility that the development of AI will be the spark that triggers this revolution. Artificial intelligence can handle healthcare data's ambiguities and complexity, improving Decision-Making (DM), Resource Allocation (RA), and patient outcomes. Fuzzy-based operations research with AI has many shortcomings, regardless of its advantages. The challenges mentioned above include the inability to comprehend clinical decisions, data privacy problems, healthcare data complexity, and the demand for advanced processing abilities. The study suggested Hybrid Computational Automation with Fuzzy Decision (HCA-FD) to resolve those challenges. The HCA-FD system's Fuzzy Logic (FL) and AI can manage the most complicated DM processes. By employing Machine Learning (ML) techniques and Fuzzy Interference System (FIS), the reliability and accuracy of the healthcare revolution can be improved. Optimizing treatment plans, managing healthcare facility resources, and predicting patient diagnosis can all be achieved via applying HCA-FD. The HCA-FD suggested automated key procedures to enhance efficiency, minimize human error, and improve healthcare services. The simulation outcomes can reveal the efficiency of HCA-FD in various healthcare operations. It can analyze unclear and complicated data and support the creation of reliable DM. As a result, it enhances both the operational efficiency and patient care. The proposed method increases the Decision-Making Accuracy ratio by 99.6%, Resource Utilization Efficiency ratio by 97.6%, Computational Efficiency ratio by 98.9%, Security ratio by 96.2%, and Predictive Analytics Accuracy ratio by 95.4% compared to other existing methods.