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Background An artificial intelligence (AI)–assisted contouring system benefits radiation oncologists by saving time and improving treatment accuracy. Yet, there is much hope and fear surrounding such technologies, and this fear can manifest as resistance from health care professionals, which can lead to the failure of AI projects. Objective The objective of this study was to develop and test a model for investigating the factors that drive radiation oncologists’ acceptance of AI contouring technology in a Chinese context. Methods A model of AI-assisted contouring technology acceptance was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model by adding the variables of perceived risk and resistance that were proposed in this study. The model included 8 constructs with 29 questionnaire items. A total of 307 respondents completed the questionnaires. Structural equation modeling was conducted to evaluate the model’s path effects, significance, and fitness. Results The overall fitness indices for the model were evaluated and showed that the model was a good fit to the data. Behavioral intention was significantly affected by performance expectancy (β=.155; P=.01), social influence (β=.365; P<.001), and facilitating conditions (β=.459; P<.001). Effort expectancy (β=.055; P=.45), perceived risk (β=−.048; P=.35), and resistance bias (β=−.020; P=.63) did not significantly affect behavioral intention. Conclusions The physicians’ overall perceptions of an AI-assisted technology for radiation contouring were high. Technology resistance among Chinese radiation oncologists was low and not related to behavioral intention. Not all of the factors in the Venkatesh UTAUT model applied to AI technology adoption among physicians in a Chinese context.

Background: Chinese public hospitals are facing 'business-like' transformation challenges in the Health Care Reform policy era, with increasing focus on lean management and efficiency. At the same time, other issues have occurred, including tense physician-patient relations, heavy workloads and high job stress which have led to low job satisfaction and burnout among medical staff. This paper aims to explore a Chinese hospital culture profile with the possibility of combining a culture of a patient wellness-centered approach and a business-like approach. Methods: A questionnaire (n=110) using convenience sampling was carried out at Sun Yat-sen University Cancer Center (SYSUCC). Observation and interview (n=8) data on the hospital's operations were also collected. Results: SYSUCC's culture profile includes clan culture (mean = 4.35), hierarchical culture (mean = 4.40) and rational culture (mean = 4.32). The Chief Clinical Professor Responsibility System is a key strategy that enables this culture profile to be successful in the hospital. Conclusion: This culture profile focuses on teamwork inside and outside of the medical teams to achieve a positive and cohesive effect, with standardized operational and formal procedures, as well as, a performance assessment system to reach the goal of operational stability, efficiency and better financial outcomes.