生成式人工智能大模型的场景治理:以医疗大模型为例OACHSSCD

Generative Artificial Intelligence(AI)represents a strategic information technology that is spearheading a new round of scientific and techno-logical revolution and industrial transformation.Given the diversity of application scenarios for large-scale AI models,scenario governance has emerged as a new trend in adaptive governance.However,problems exists,such as the lack of sufficient theoretical analysis and practical guidance on how to conduct scenario governance within vertical sectors and how to achieve appropriate scenario classification.Focusing on the healthcare sector as a vertical domain,this study innovatively constructs a classification model that balances both horizontal application areas and vertical application depths,providing a theoreti-cal classification framework for scenario governance.By employing multi-case studies and typological methods,it examines the technical and non-technical bottlenecks in different application scenarios of large-scale AI models,uncovering both common patterns and differential manifestations of developmental bottlenecks across various scenarios.Based on these findings,the study proposes systematic scenario governance pathways in terms of the provision of high-quality datasets,breakthroughs in foundational algorithms and key technologies,and the formulation of tailored social application rules.