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.

中文翻译：

---

人工智能在医疗保健生态系统中的可就业性含义：随时准备应对

目的

就专业人员的就业能力以及他们准备利用AI的好处而言，人工智能（AI）的介入提出了未来工作前景的问题。这项研究的目的是通过分析卫生保健领域的问题来认识到AI对就业能力的影响，如果不解决这些问题，这些问题可能会抑制AI干预及其普及性的可能性（康奈尔大学，INSEAD和WIPO，2019年）。

设计/方法/方法

为了深入了解这些问题，已采用总解释结构建模，交叉影响矩阵乘法应用于分类和路径分析的方法来了解影响卫生保健部门就业能力的关键因素的作用。

发现

这项研究主要探讨了就业能力关键因素的驱动依赖性，并显示了等级关系。它还讨论了可以采取的措施，如果采取这些措施，可以在人工智能的干预下提高医疗保健部门的就业能力。

研究局限/意义

可聘用性也对卫生保健服务提供的生产率产生影响，这可能为卫生保健组织的管理层提供全面的机会，以预测人力资源和技术资源的分配和培训。

创意/价值

本文试图分析AI干预和其他驱动因素（运营变化，定制培训干预，学习的开放性，对技术的态度，与工作相关的技能和AI知识），以分析其随着需求的变化对就业能力的影响。由于人工智能的干预，它建立了影响卫生保健部门就业能力的关键因素之间的等级关系。