Table 1 Partial summary of research on the acceptance of AI in healthcare.
Researcher | Object | Theoretical framework | Methodology | Country | Key outcomes |
|---|---|---|---|---|---|
Alhashmi et al.39 | Artificial intelligence projects in health sector | TAM | Partial least squares structural equation modeling (PLS-SEM) | United Arab of Emirates (UAE) | Managerial, organizational, operational and IT infrastructure factors have a positive impact on (AI) projects perceived ease of use and perceived usefulness. |
Lin et al.67 | AI applications in hospitals | TAM | SEM | China | SN, PEOU, PU, and attitude can predict the intention of healthcare professionals to learn and use AI applications to support precision medicine. |
So et al.41 | Artificial intelligence | TAM | PLS-SEM | Malaysia | There is a significant relationship between perceived usefulness and acceptance of subjective norms regarding AI. |
Fan et al.66 | Artificial intelligence-based medical diagnosis support system (AIMDSS) | UTAUT | PLS-SEM | China | Initial trust is an important predictor for healthcare professionals to adopt AMIADS, and it is also an intermediary between existing factors in the UTAUT and behavioral intentions to use AMIADS. |
van Bussel et al.42 | Virtual assistant | UTAUT | SEM | The Netherlands | Performance expectancy, effort expectancy, social influence, and trust significantly influence the behavioral intention of using virtual assistants. |
Prakash and Das68 | Intelligent clinical diagnostic decision support systems | UTAUT | PLS-SEM | India | Performance expectancy, effort expectancy, social influence, initial trust, and resistance to change predict intention to use. |
Zarifis et al.69 | Health insurance that explicitly utilizes AI | TAM | PLS-SEM | UK | The perceived usefulness, trust, and personal information privacy concern (PIPC) all affect the use of health insurance. |
