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论文编号：	13207
作者编号：	2120202865
上传时间：	2022/6/7 15:06:11
中文题目：	任务类型和调节聚焦对 AI 医疗诊断接受意愿的影响 ——“风险-收益”权衡的中介作用
英文题目：	The Effect of Task Type and Regulatory Focus on Willingness to Accept AI Medical Diagnosis——The Mediating Role of “Risk and Benefit”Trade-off
指导老师：	杜建刚
中文关键字：	AI 医疗；任务类型；调节聚焦；感知风险；接受意愿
英文关键字：	AI medical treatment; Task type; Regulatory focus; Perceived risk; Acceptance
中文摘要：	医疗人工智能在图像处理、癌症检测和手术方面取得了巨大进展。提高人工 智能在医疗领域的应用，对缓解医疗资源紧张、减少误诊、提高医疗服务满意度 具有重要意义。但由于技术局限、算法厌恶和技术“黑箱”，由于担心自身独特 性被忽视等问题，用户对 AI 医疗尤其是自主化的 AI 医疗还存在较大的抵制。 学者们开始关注影响 AI 医疗接受意愿的内在因素。 但现有人工智能医疗服务的研究集中于医疗技术的发展、伦理安全和隐私 问题，研究 AI 医疗接受意愿的影响因素的文献仍较少。且现有研究多从技术接 受模型、思想感知理论出发，忽略了任务技术匹配的作用和个体特征的作用。基 于此，本研究从医疗诊断的任务类型和动机领域的调节聚焦出发，纳入感知风险 理论，构建了 AI 医疗诊断接受意愿模型。本文通过三项实验，共 577 个有效数 据探究了个体对于自主化 AI 医疗诊断接受度的差异，以期能为理论研究和智能 化医疗实际应用做出贡献。 研究结果表明，相比于临床诊断任务，个体对 AI 执行影像的或病理的辅助 诊断任务时有更高的接受意愿。另外，任务类型和调节聚焦会产生交互作用，促 进聚焦的个体对自主化 AI 进行辅助诊断的接受意愿要高于预防聚焦的个体，但 预防聚焦个体对 AI 执行两种不同诊断任务时的接收意愿无差异。深入研究发现， “风险-收益”权衡在任务类型和调节聚焦对接受意愿的影响中起到中介作用。 促进聚焦的个体对 AI 执行辅助诊断的“风险-收益”权衡值要显著低于 AI 执行 临床诊断时的权衡值，但预防聚焦的个体对 AI 执行不同诊断任务时的“风险-收 益”权衡值无差异。 最后，本研究对实证检验的结果进行了讨论，指出本文的研究贡献，并阐述 了对医疗服务行业的管理启示。然后指出了本研究在过程和内容的不足之处，以 及未来研究的展望。
英文摘要：	Medical AI has made great strides in image processing, cancer detection and surgery. Improving the application of Artificial Intelligence in the medical field is of great significance to relieve the strain of medical resources, to reduce misdiagnosis and to improve the satisfaction of medical service. However, due to technical limitations, algorithm aversion and technical "black box", as well as concerns about their own uniqueness being ignored, users still have great resistance to autonomous AI medical treatment. Scholars have begun to focus on the internal factors that affect the willingness to accept AI for medical treatment. However, the existing research on AI medical services focuses on the development of medical technology, ethical safety and privacy. The literature on the influencing factors of AI medical acceptance intention is still less. In addition, most of the existing researches start from the technology acceptance model and thought perception theory, ignoring the role of task technology fit and the role of individual characteristics. Based on this, this study constructs an AI medical diagnosis acceptance willingness model based on the regulatory focus in motivation field, task type and the theory of perceived risk. Through three experiments and 577 effective data, this paper explores individual differences in acceptance of autonomous AI medical diagnosis, hoping to make contributions to theoretical research and practical application of intelligent medical treatment. The results show that compared with clinical diagnostic tasks, individuals have a higher willingness to accept AI when performing imaging or pathological auxiliary diagnostic tasks. In addition, task type and regulatory focus had an interaction effect, and the receptive willingness of the promotion-focused individuals to autonomous AI auxiliary diagnosis was higher than clinical diagnosis. But the receptive willingness of the prevention-focused individuals to perform two different diagnostic tasks for AI was not different. Further research found that “Risk and Benefit” Trade-off plays a mediating role in the effect of task type and moderating focus on acceptance intention. The “Risk and Benefit” Trade-off of the promotion-focused individuals performing auxiliary diagnosis to AI was significantly lower than that of the AI performing clinical diagnosis. But there was no difference in the “Risk and Benefit” Trade-off of the prevention-focused individuals performing different diagnostic tasks to AI. Finally, this study discusses the results of empirical tests, points out the contribution of this study, and expounds the management implications for the medical services. Then it points out the deficiencies in the process and content of this study, as well as the prospect of future research.
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