Employability implications of artificial intelligence in healthcare ecosystem: responding with readiness

Abstract

Purpose Intervention of artificial intelligence (AI) has brought up the issue of future job prospects in terms of the employability of the professionals and their readiness to harness the benefits of the AI. The purpose of this study is to recognize the implications of AI on employability by analyzing the issues in the health-care sector that if not addressed, can dampen the possibilities offered by AI intervention and its pervasiveness (Cornell University, INSEAD, and WIPO, 2019). Design/methodology/approach To get an insight on these concerns, an approach of total interpretive structural modelling, cross impact matrix multiplication applied to classification and path analysis have been used to understand the role of the critical factors influencing employability in the health-care sector. Findings This study primarily explores the driving-dependence power of the critical factors of the employability and displays hierarchical relationships. It also discusses measures which, if adopted, can enhance employability in the health-care sector with the intervention of AI. Research limitations/implications Employability also has an impact on the productivity of the health-care service delivery which may provide a holistic opportunity to the management in health-care organizations to forecast the allocation and training of human resources and technological resources. Originality/value The paper attempts to analyze AI intervention and other driving factors (operational changes, customized training intervention, openness to learning, attitude toward technology, job-related skills and AI knowledge) to analyze their impact on employability with the changing needs. It establishes the hierarchical relationship among the critical factors influencing employability in the health-care sector because of the intervention of AI.

The Role of Methods and Applications of Artificial Intelligence Tools in the Field of Medicine to Diagnose and Discover Various Diseases

“…In short, there are many questions that must be addressed in order to move in line with the development of the technology and to obtain the benefits of its practical implementation. It was mentioned in some studies such as studies [1,6,8] and [10] the application and use of artificial intelligence systems in the medical field, but it was used to a limited extent, such as obtaining some statistics and results or some information. For example, but not limited to, the study [1] proposed technology was a multi-layer (Perceptron) training, which is a type of artificial neural network with a backpropagation training algorithm to diagnose and predict five blood disorders, through the results of the complete blood count test.…”

“…For example, but not limited to, the study [1] proposed technology was a multi-layer (Perceptron) training, which is a type of artificial neural network with a backpropagation training algorithm to diagnose and predict five blood disorders, through the results of the complete blood count test. Study [6] stated that a diagnostic system was applied to detect the type of the brain tumor and its classification among three types in the magnetic resonance image. Study [8] stated that the use of artificial intelligence methods such as neural networks can significantly improve the accuracy of diagnosis.…”

“…Many medical centers have developed algorithms, methods, and tools of artificial intelligence for their facilities in the field of diagnosis and disease discovery. We mention, for example, some of the proposed methods and tools that were mentioned in this article, such as the proposed technology, multi-layer training (Perceptron), a type of artificial neural network with an algorithm Reverse diffusion training for diagnosis and prediction, which was mentioned in study [1], as well as industrial neural networks and their applications, which were mentioned in studies [1], [6] and [8]. In addition, study [3] mentioned some of the applications used in this field.…”

“…There are also several articles that used fuzzy logic, machine learning, and deep learning, respectively. Study [6] stated that the proposed algorithm was first applied to the use of SOM self-organizing maps to segment the image and reveal the tumor area followed by image processing and optimization processes and tumor characteristics extraction. Forward propagation neural networks were also used in order to diagnose the type of tumor based on the detected characteristics.…”

“…Based on Jain et al [6], This paper aimed at knowing how the tools and methods of artificial intelligence can be applied in improving this field of diagnosis. The proposed technology includes training (Perceptron) multi-layered and a kind of artificial neural network with backpropagation training algorithm to diagnose and predict five blood disorders, through the results of complete blood count (CBC).…”