Empirical Analysis for Improving Food Quality Using Artificial Intelligence Technology for Enhancing Healthcare Sector - S. K. UmaMaheswaran; Gaganpreet Kaur; A. Pankajam; A. Firos; Piyush Vashistha; Vikas Tripathi; Hussien Sobahi Mohammed (Journal of Food Quality)
Artificial intelligence or AI has a wide range of applications in healthcare and food industries. AI helps in different ways in medical industries, such as analysing the disease progression rate, effective prediction of treatment method, and proper disease diagnosis. Advantages of artificial intelligence in the food business include enhanced customer accessibility, improved technological innovation, readily accessible client requirements and comments, strategic advantage through unique products, and plenty others. Different AI technologies such as “Machine Learning (ML),” “Neural Language Processing (NLP),” “Rule-Based Expert Systems (RESs),” “Deep Learning (DL),” and so on are used in healthcare and food industries for big “medical data” analysis. This study has applied three critical variables to measure the application of AI in enhancing food quality (viz., usage of machine learning models, NLP models, etc.). This study has stated that these models support in enhancing the overall food quality in an effective manner. The present research analyses the importance of these AI technologies in enhancing service quality in healthcare and food industries. A primary survey-based data analysis has been done with 153 individuals taken from healthcare industries. Moreover, statistical analysis has been done in this research with SPSS software. Four independent variables are taken in this research, which are ML, NLP, RES, and DL. The service quality of healthcare has been taken as a dependent variable, and the effect of independent variables on “enhancing healthcare service” has been analysed. Secondary thematic analysis has been done to justify primary data. The results show that 43.79% of the individuals have supported DL and 56.86% have supported the treatment prediction ability AI. 37.9% of the individuals have also supported AI over traditional medications. Further analysis has shown that independent variables ML, DL, NLP, and RES have a strong positive correlation with improving SQ. These results have been justified by secondary journals, and it is proved that AI technologies enhance the service quality in healthcare and food sectors.