With the rapid expansion of development and application of Artificial Intelligence (AI) technology in the healthcare sector, and the upcoming implementation of the "Framework Act on Artificial Intelligence" in South Korea in 2026, social discussions r...
With the rapid expansion of development and application of Artificial Intelligence (AI) technology in the healthcare sector, and the upcoming implementation of the "Framework Act on Artificial Intelligence" in South Korea in 2026, social discussions regarding the pace of technology adoption and the level of regulation are actively underway. As AI intervenes in the entire process of medical care, it is necessary to go beyond discussions centered on the state, government, and experts to understand the perspectives of the public, the actual users, and reflect them in policy decisions. Nevertheless, there is a lack of empirical research in Korea that simultaneously addresses perceptions of the adoption pace and the necessity of regulation for healthcare AI, while integrally analyzing generational differences and related factors. Accordingly, this study identified the level of perception among the Korean public regarding the pace of healthcare AI adoption and the necessity of regulation. It further examined factors related to these perceptions, including socio-demographic characteristics, technological experience, knowledge and attitudes toward AI, and perceived value and risk of AI. Through this, a comparative analysis was conducted among the Baby Boomer (born 1950-1964), Gen X (born 1965-1980), and MZ (born 1981-2004) generations.
This study performed a secondary analysis of data from the 「Survey on Perceptions of AI and Aging Society 2023」, conducted by the SNU AI in Health & Care Center through a professional survey agency on 1,000 adults nationwide aged 19 and older. Variables were selected according to a conceptual framework based on previous literature to identify the level of perception and related factors regarding AI adoption pace and regulatory necessity, and to analyze generational differences. Descriptive analysis and multiple linear regression analysis were conducted in accordance with the research objectives.
The results indicated that the Korean public perceives a need to increase the pace of healthcare AI adoption above a certain level, while simultaneously recognizing that regulations should be strengthened. Regarding the necessity of regulation, risk perception was the strongest related factor; simultaneously, the perception of AI benefits also tended to increase the perceived need for regulation, suggesting that perceptions of adoption and regulation function complementarily rather than conflictually.
By generation, the Baby Boomer generation perceived the greatest need to accelerate the adoption pace, which was significantly lower in the MZ generation. The perception of regulatory necessity was highest in Gen X and lowest in the Baby Boomer generation. This is interpreted to reflect differences in perceived health status, medical needs, and the intensity of expected utility from healthcare AI across generations. In particular, the older generation perceives AI as a means to fill gaps in medical and nursing care, whereas the younger generation tends to place more importance on the normative and institutional conditions of the technology. Multivariate analysis revealed that perceptions of adoption pace were significantly associated with generation, education level, subjective health status, technological experience, attitudes toward AI utilization, perceived benefit, risk perception, and confidence in effectiveness. Perception of regulatory necessity was significantly associated with generation, perceived AI benefit, and AI risk perception. Across all generations, higher confidence in the benefits and effectiveness of AI correlated with responses favoring accelerated adoption, while higher risk perception was associated with a lower perceived need for rapid adoption and a higher perceived need for regulation. These results suggest the necessity of establishing a responsible and explainable healthcare AI system.
This study has limitations as a cross-sectional analysis, with the possibility of bias due to the nature of self-reported online surveys, and the exclusion of relevant factors not included in the original questionnaire. Nevertheless, this study is significant as the first generational comparison study to simultaneously analyze the pace of healthcare AI adoption and the necessity of regulation, and in identifying public perception levels prior to the enforcement of the Act. Through this, it is expected that this study will contribute to a more sophisticated understanding of the generational perception structure regarding the pace of healthcare AI adoption and regulatory necessity, providing basic data for designing healthcare AI policies and governance, as well as for follow-up research that can support generation-specific social consensus.