According to the benefits in safeguarding and transferring medical information, illness assessment, evaluation of “Magnetic Resonance Mapping” images, and certain other disciplines, blockchain and machine learning (ML) technology has significantly piqued attention in the healthcare domains. Formerly, those chores have been performed out along with individuals; eventually, individuals acquired attraction because to its precision and efficiency. The proposed study will examine the activities and possible capabilities of learning algorithms and blockchain in the healthcare professions focusing on these fascinating facts. Primary and secondary data analysis has been executed, with primary analysis method consisting of a survey of 150 randomly picked medicine professionals with expertise in machine learning and blockchain. They gave their answers that were being subsequently transferred to figures and employed as response variable in SPSS examining. The length of time where learning and blockchain have been used in medicine is really the independent factor. To better understand the primary and small hurdles of integrating machine learning and blockchain, a correlation investigation was done. Thereafter, secondary methodology is employed to validate the primary study results.
In the current information and technology era, business enterprises are focusing in performing the process effectively by reducing the waiting time in completing the work, reduce latency and deploy the resources effectively so as to service the patient, medical practitioners, societies, and other stakeholders in an efficient manner. Hence, several organisations are using the emerging technologies so as to obtain high performance and enhance competitive edge. The advancement in machine learning, deep learning, business analytics, etc. enables the health care industry to identify the patterns based on the data collected and create a pivotal position and enhance revenues and profits in a sustainable manner. Machine learning models are considered as computational algorithms which will enable in collected the data, analyze them, and provide the necessary reports to the experts and management in order to make informed decision making. The application of advanced machine learning enables the organisation to process the image effectively, recognize the voice and enable in servicing the customers, process the available data, and identify the patterns so as to make informed decision making. The basic purpose of the study is to analyze the overall implementation of advanced machine learning approaches towards health care services for providing enhanced services, better patient engagement, and support in creating better life for them, the researchers intend to collect the closed-ended questionnaire from employees in different medical centers covering: apprehend the nature of designing and implementation of machine learning approaches in the organisation and understand the effectiveness of these tools in enhancing the sustainable growth and development of the organisation.
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.
The current decade has seen an increased usage of high-end digital technologies like machine learning in the field of health care services which enable in supporting and performing different functions with less or no human interventions. The application of machine learning tools in the orthopedic area is gaining more popularity as it can support in analyzing the issues in a more comprehensive manner, provide accurate data, support in forecasting the pattern. It enables offering critical information for taking quick decisions by the medical practitioners in order to enhance the health and dietary care service delivery. The ML tools can support in collecting patient centric data related to orthopedic surgery and also estimate the postoperative complications, level of treatment modalities to be provided, and guide the medical practitioners in taking effective clinical device decisions. The ML approach also supports in providing prediction methods of implementing the ortho surgical outcomes. Furthermore, it can also guide in making better treatment procedures, forecast the patterns, and stream the health care management services for better patient recovery. This study implements a quantitative research approach which will support in sourcing the data from the respondents who are currently working as medical practitioners, orthopedic experts, and radiologists who use ML-based models in making critical decisions related to orthopedic surgery. The researchers chose nearly 149 respondents, and the information was analysed using the IBM SPSS package for gaining critical interpretation. The major analyses cover descriptive analysis, regression analysis, and analysis of variances.
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