中国医院人工智能应用的现实瓶颈与协同发展路径OA

Objective To delineate the current status,core bottlenecks and development needs of artificial intelligence(AI)adoption in Chinese hospitals,thereby providing empirical evidence for the large-scale,standardized and high-quality adop-tion of medical AI.Methods Based on valid questionnaires from 205 hospitals across 28 provinces,descriptive statistics and stratified analysis were conducted to systematically examine the regional distribution,tier-based disparities differences and scenar-io-specific adaptation of hospital AI adoption.Results AI had been adopted in 94.2%of surveyed hospitals,but only 12.6%achieved extensive deployment.The on-premise deployment rate of large AI models was 51.7%.Adoption showed a"clinical-heavy,management-light"pattern.Major constraints included poor data quality,talent shortages and high costs.Conclusion Hospital AI adoption in China remains at an early exploratory stage,characterized by marked regional and tier-based disparities,scenario imbalance,suboptimal outcomes and emerging risks.It is recommended that governments,enterprises and hospitals col-laborate to refine standards,deepen medical-engineering integration,and strengthen data governance and workforce development.