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• W3198596472 endingPage "103524" @default.
• W3198596472 startingPage "103524" @default.
• W3198596472 abstract "Artificial intelligence-based clinical diagnostic decision support systems promise transformational improvements in doctors’ efficiency and accuracy. Nevertheless, low adoption rates suggest that this innovation could fail without adequate uptake. This study uses a mixed-methods approach to develop and test a model based on theories of Unified Theory of Acceptance and Use of Technology, status quo bias, and technology trust. The results show that performance expectancy, effort expectancy, social influence, initial trust, and resistance to change predict intention to use. Further, inertia, perceived threat, and risks (medico-legal and performance) determine resistance to change. Measures for alleviating resistance and improving adoption are proposed." @default.
• W3198596472 created "2021-09-13" @default.
• W3198596472 creator A5048474556 @default.
• W3198596472 creator A5084138407 @default.
• W3198596472 date "2021-11-01" @default.
• W3198596472 modified "2023-10-02" @default.
• W3198596472 title "Medical practitioner's adoption of intelligent clinical diagnostic decision support systems: A mixed-methods study" @default.
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